Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-30T09:18:04.841Z Has data issue: false hasContentIssue false

Riding the (brain) waves! Using neural oscillations to inform bilingualism research

Published online by Cambridge University Press:  04 November 2022

Eleonora Rossi
Affiliation:
University of Florida
Sergio Miguel Pereira Soares
Affiliation:
University of Konstanz Max Planck Institute for Psycholinguistics
Yanina Prystauka
Affiliation:
UiT the Arctic University of Norway
Megan Nakamura
Affiliation:
University of Florida
Jason Rothman*
Affiliation:
UiT the Arctic University of Norway Universidad Nebrija
*
Address for correspondence: Jason Rothman Department of Language and Culture UiT the Arctic University of Norway 9019 Tromsø, Norway jason.rothman@uit.no
Rights & Permissions [Opens in a new window]

Abstract

The study of the brains’ oscillatory activity has been a standard technique to gain insights into human neurocognition for a relatively long time. However, as a complementary analysis to ERPs, only very recently has it been utilized to study bilingualism and its neural underpinnings. Here, we provide a theoretical and methodological starter for scientists in the (psycho)linguistics and neurocognition of bilingualism field(s) to understand the bases and applications of this analytical tool. Towards this goal, we provide a description of the characteristics of the human neural (and its oscillatory) signal, followed by an in-depth description of various types of EEG oscillatory analyses, supplemented by figures and relevant examples. We then utilize the scant, yet emergent, literature on neural oscillations and bilingualism to highlight the potential of how analyzing neural oscillations can advance our understanding of the (psycho)linguistic and neurocognitive understanding of bilingualism.

Type
Review Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Introduction

The presence of more than one language in a single mind – i.e., bilingualism – has been shown to have consequences for (both) language(s) at the levels of linguistic representation, production and processing (e.g., Dussias & Sagarra, Reference Dussias and Sagarra2007; Verhoeven, van Leeuwe & Vermeer, Reference Verhoeven, van Leeuwe and Vermeer2011; Tsimpli, Peristeri & Andreou, Reference Tsimpli, Peristeri and Andreou2016; Schulz & Grimm, Reference Schulz and Grimm2019; Leivada, Westergaard, Duñabeitia & Rothman, Reference Leivada, Westergaard, Duñabeitia and Rothman2021a). And while replication is consistently observed across studies, (degree of) dual language experience has often been shown to associate with behavioral effects on tasks tapping into domain-general cognitive functions (see Bialystok, Reference Bialystok2021 for a recent review/opinion paper). Moreover, brain structure, functional connectivity at rest as well as recruitment patterns on tasks have been shown to calibrate to degree of bilingual language engagement (see Pliatsikas, Reference Pliatsikas, Schwieter and Paradis2019 for review). Substantial evidence supports the hypothesis that, when observed, linguistic and/or cognitive bilingual effects stem from underlying language competition and the cognitive demands to manage it (see Kroll & Bialystok, Reference Kroll and Bialystok2013; Kroll, Gullifer, McClain, Rossi & Martín, Reference Kroll, Gullifer, McClain, Rossi, Martín and Schweiter2015; Kroll, Bobb, Misra & Guo, Reference Kroll, Bobb, Misra and Guo2008; Rothman, Alonso & Puig-Mayenco, Reference Rothman, Alonso and Puig-Mayenco2019; Bialystok & Craik, Reference Bialystok and Craik2022).

We know that individual-level factors are deterministic for bilingual language exposure and each bilingual's engagement with their languages over time. It should come as no surprise, then, that these same factors condition/delimit opportunities for bilingual outcomes, be they linguistic or neurocognitive (Luk & Bialystok, Reference Luk and Bialystok2013; Kupisch & Rothman, Reference Kupisch and Rothman2018; DeLuca, Rothman, Bialystok & Pliatsikas, Reference DeLuca, Rothman, Bialystok and Pliatsikas2019b; Leivada, Mitrofanova & Westergaard, Reference Leivada, Mitrofanova and Westergaard2021b). Many variables influence the individual likelihood for (any at all) or degree of linguistic and domain general neurocognitive adaptations, including but not limited to: (i) age-of-acquisition, (ii) duration of bilingualism, (iii) patterns of using both languages (separately and/or how they are interspersed in the same discourse) across an array of domains, (iv) size of the speech communities, (v) density of an individual's linguistic social networks, (vi) expected/normative choice and prestige of the languages in the society, (vii) what in the input (quality) can be taken in to form grammatical representations, (vii) levels of activation that relate to speed of processing and cross-linguistic influence. In other words, as opposed to an actual state of being, bilingualism is a spectrum of experience and opportunity. Where an individual's experiences place them along this spectrum determines what outcomes in language and mind/brain can be expected.

Over the past decade in particular, (psycho)linguistic and neurocognitive work on bilingualism has honed in on trying to identify and unpack the internal and external exponents of bilingualism, inclusive of how they interact, to make sense of the variation seen across bilingual groups and individuals. In this sense, the complexities and idiosyncrasies of bilingual language and cognitive systems, patterns of linguistic usage and processing as well as physical brains can serve as a rather special testing ground for uncovering a deeper understanding of the brain's organization (for language and of cognitive systems) more generally. Doing so requires diverse methodologies, from offline and online behavioral methods to various types of neuroimaging.

As we will discuss herein, electroencephalography (EEG) has proven to be an especially important tool in the psycholinguists’ and cognitive scientists’ language arsenal. As a neuroimaging method with excellent temporal resolution, it allows for a glimpse underneath the proverbial hood – that is, into how the brain processes information in real time. As such, it is an instrument that can be used for examining the processing of language itself, examining potential effects for non-linguistic general cognitive processing as well as the brain's functional connectivity that might be affected by (dual) language experience. Most, if not all, hypotheses regarding bilingual linguistic systems, processing and neurocognition offer predictions amendable to EEG. For example, if bilinguals have distinct representations for language or process language differentially as compared to monolinguals, this should show up as distinctions in neural corollaries of processing. Changes in brain structure and connectivity are reflected in changes in EEG patterns (McCarley, Nakamura, Shenton & Salisbury, Reference McCarley, Nakamura, Shenton and Salisbury2008; Chang, Liu, Chen, Liu & Duyn, Reference Chang, Liu, Chen, Liu and Duyn2013). And so, if bilingual minds/brains adapt to accommodate the cognitive demands of being bilingual, then this should be measurable in the bilingual brain's neuroelectric activity during task performance or even at its baseline resting activation, relative to monolinguals and, perhaps more revealing, to one another based on individual degree of bilingualism. Although EEG is a well-used method in language research in general (Beres, Reference Beres2017), most relevant studies use only one (of several) type(s) of EEG analyses – namely, Event Related Potentials (ERPs). While very good work has been done with ERPs, our focus here will be to discuss how and why examining the understudied oscillatory signatures of the bilingual brain is, at least, as informative for research on (bilingual) language processing and its effects on domain-general cognition. In fact, as we will show, doing so opens up new and exciting questions that ERPs are not best positioned to address. Relative to ERPs, the analysis of oscillatory dynamics is better suited for capturing the multidimensionality of language processing, which happens in synchrony with a number of domain general cognitive processes that enable it to unfold in a fluid and timely manner (e.g., working memory maintenance, memory encoding and retrieval, attention, prediction, maintenance of the current cognitive set). Indeed, investigating the properties of the spectral signal over time is a powerful analysis tool because it enables one to capture different subcomponents of complex cognitive processes that originate from the synchronization/desynchronization of neuronal activity at certain frequencies, times, and neural locations (Ward, Reference Ward2003). Crucially, these methods highlight how distinct brain oscillations contribute to the formation of neural assemblies, which work in synchrony to support different aspects of linguistic processing (Bastiaansen, Mazaheri & Jensen, Reference Bastiaansen, Mazaheri and Jensen2012). Given that other dimensions of the EEG signal (such as frequency, time, and phase) have been actively utilized in broader fields of cognitive neuroscience for a long time, taking advantage of this pre-existing knowledge enriches and advances the interpretation of the findings in the field of bilingualism while drawing it ever closer to other fields of cognitive neuroscience.

With the above as our backdrop, the main goal of this paper is to present and discuss an array of methodological tools gravitating around the construct of neural oscillations to help inform theories and clarify misconceptions and contradictory findings in bilingualism research. Importantly, we do not do so by diving too deeply into any specific analytical, computational or mathematical approach, even though we will provide extensive descriptions of the various methodologies and refer the reader to additional technical sources (see Cole & Voytek, Reference Cole and Voytek2019; Gross, Reference Gross2014; Hurtado, Rubchinsky & Sigvardt, Reference Hurtado, Rubchinsky and Sigvardt2004; Maris & Oostenveld, Reference Maris and Oostenveld2007; Oostenveld, Fries, Maris & Schoffelen, Reference Oostenveld, Fries, Maris and Schoffelen2011; Wacker & Witte, Reference Wacker and Witte2013 for examples of toolkits on analysis methods for EEG and neural oscillations). Rather, we focus on higher-level theoretical frameworks surrounding brainwaves. First, we introduce the theory behind brain oscillations, i.e., what they are, how they can be measured, what they are good for and how they differentially inform brain mechanisms in comparison to the more commonly used ERPs. We then apply this knowledge into the context of bilingualism research with particular interest on: bilingual language processing and neurocognitive correlates of bilingualism. We conclude by summarizing what we already know from the nascent literature regarding bilingualism and brainwaves, taking it further by offering potential new exploratory avenues for the field.

Brain oscillatory measures: an introduction

Our everyday neurocognitive activities (including language comprehension and production) result from the brain absorbing, processing and integrating information surrounding us in real time. This is achieved via complex, continuous and bi-directional transformations of chemical to electrical signals in the brain, enabled by neuronal activity and facilitated by glial cells. The electrical signal of a single neuron (or local small circuits) is virtually undetectable unless it is directly recorded – for example, as multiunit activity (MUA), local field potentials (LFPs) or electrocorticography (ECoG) (Burns, Santaniello, Yaffe, Jouny, Crone, Bergey, Anderson & Sarma, Reference McCarley, Nakamura, Shenton and Salisbury2014; De Hemptinne, Swann, Ostrem, Ryapolova-Webb, San Luciano, Galifianakis & Starr, Reference De Hemptinne, Swann, Ostrem, Ryapolova-Webb, San Luciano, Galifianakis and Starr2015; Kajikawa & Schroeder, Reference Kajikawa and Schroeder2011; Land, Engler, Kral & Engel, Reference Land, Engler, Kral and Engel2013; Nuwer, Reference Nuwer2010). In contrast, the resulting sum of the postsynaptic activity of thousands of neurons composes the EEG signal, picked up by electrodes placed on the scalp (Holmes & Khazipov, Reference Holmes, Khazipov, Blum and Rutkove2007; Kirschstein & Köhling, Reference Kirschstein and Köhling2009). More specifically, EEG measures the superposition of postsynaptic currents produced by similarly oriented pyramidal neurons in the outer layers of the cerebral cortex (Olejniczak, Reference Olejniczak2006). Spontaneous fluctuations in the intracellular membrane potentials (caused presumably by voltage-gated ion channels/potentials) of these neurons (Wilson & Kawaguchi, Reference Wilson and Kawaguchi1996; Fernandez, Noueihed & White, Reference Fernandez, Noueihed and White2019) can synchronize at particular frequencies resulting in the phenomenon of neural oscillationsFootnote 1.

The EEG signal can provide frequency-specific information by means of a spectral analysis. In other words, using simple oscillatory functions (cosines and sines), the raw EEG signal can be analyzed by applying a series of mathematical (Fourier) transformations to decompose the temporal information into its constituent frequencies. There are five main recognized frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8-12 Hz), beta (13–30 Hz) and gamma (30–150 Hz) (Figure 1). Historically, they have been defined based on their distinguishing characteristics such as frequency, amplitude, morphology, topography, reactivity, etc. (Shackman, McMenamin, Maxwell, Greischar & Davidson, Reference Shackman, McMenamin, Maxwell, Greischar and Davidson2010; Brazier, Reference Brazier1961).

Figure 1. Spectrogram of (log) power over frequency depicting the five (to six) classical frequency bands appearing in different colors.

Each frequency band has been associated with a number of perceptual and cognitive processes (e.g., Buzsáki, Reference Buzsáki2006). Given the large number of recognized cognitive processes and the limited number of frequency bands, there is obviously no one-to-one mapping between a given band and a particular cognitive function. Neural oscillations are more likely to reflect more basic local computations (e.g., feedback, local encoding; see Siegel, Donner & Engel, Reference Siegel, Donner and Engel2012; Donner & Siegel, Reference Donner and Siegel2011), which, recruited by different brain regions, result in the cascading processes needed for various higher-level cognitive tasks. For example, higher frequencies (e.g., gamma) have been associated with local neural processes, whereas lower-frequency bands (e.g., theta) are argued to reflect longer-range cortical communication (Von Stein & Sarnthein, Reference Von Stein and Sarnthein2000).

There are different ways to examine the spectral content of the EEG signal. One approach is to look at the power spectral density (or simply power), i.e., the amount of rhythmic activity within each frequency band, either across time (time-frequency representation, TFR) or averaged for the time-window of interest. Increase in power at a given site (i.e., single electrode or clusters of electrodes) reflects local synchronous activation within neural assemblies, i.e., distributed local networks transiently linked to perform some basic computations (Varela, Lachaux, Rodriguez & Martinerie, Reference Varela, Lachaux, Rodriguez and Martinerie2001). Alternatively, one could examine mean coherence (or functional connectivity), by determining the degree of frequency band phase-alignment across distinct recording sites (single electrodes or clusters of electrodes) at the scalp or in the source space (see, Figure 2). While some researchers have raised concerns about determining functional connectivity via EEG (Marinazzo, Riera, Marzetti, Astolfi, Yao & Valdés Sosa, Reference Marinazzo, Riera, Marzetti, Astolfi, Yao and Valdés Sosa2019), coherence has been interpreted, nonetheless, as reflecting increased synchrony between electrode sites, emerging when there is a stable phase difference between two (or more) recording sites (Varela et al., Reference Varela, Lachaux, Rodriguez and Martinerie2001; Jensen & Mazaheri, Reference Jensen and Mazaheri2010). Both power and coherence analyses can be applied to data recorded during any given task-performance (for example, linguistic or cognitive) or at rest.

Figure 2. (A) Graphical example of mean coherence or functional connectivity (pink line – see correspondent coherence Matrix highlighted values in panel B) in the gamma band between electrode scalp regions. Coherence was computed by grouping electrodes into five regions of interest (MF = Medial Frontal, LFT = Left Fronto-Temporal, RFT = Right Fronto-Temporal, LP = Left Parietal, RP = Right Parietal). Adapted from Pereira Soares et al. (Reference Pereira Soares, Kubota, Rossi and Rothman2021). (B) Hypothetical mean phase coherence matrix of Figure 2A. Mean phase coherence represents statistic interdependencies (usually) between electrodes of pre-specified regions of interest (ROIs) and the degree of functional connectivity between them (Anderson & Perone, Reference Anderson and Perone2018; van Diessen et al., 2015). Mean coherence is expressed as a matrix of values in the 0 to 1 range (the closer the value is to 1, the stronger is the connectivity). Mean coherence can also be calculated between adjacent electrodes (i.e., in the same ROI). This, however, was not the case in this example (see grey cells).

While time-frequency representations (TFRs) reflect changes in the amount of power for a time-locked event of interest as the cognitive process under investigation unfolds (see Figure 3) (Gröchenig, Reference Gröchenig2001; Sejdić, Djurović & Jiang, Reference Sejdić, Djurović and Jiang2009; Papandreou-Suppappola, Reference Papandreou-Suppappola2018), resting state EEG (rs-EEG) refers to data recorded at wakeful rest. As it is not associated with any particular task performance, it is argued to capture one's intrinsic neurophysiological activity. Thus, task-free brain oscillatory activity has been associated with overall brain functioning. As such, it is taken to be an indicator of neural readiness, a primary facilitator for the integration of past information into the brain networks for further (future) processing (e.g., Raichle & Snyder, Reference Raichle and Snyder2007). Rs-EEG power activity is relatively stable over shorter time frames. However, it does shift with age in larger time-spans, accompanying general brain development from infancy through cognitive aging (Anderson & Perone, Reference Anderson and Perone2018; Boersma, Smit, de Bie, Van Baal, Boomsma, de Geus, Delemarre-van de Waal & Stam, Reference Boersma, Smit, de Bie, Van Baal, Boomsma, de Geus, Delemarre-van de Waal and Stam2011). Rs-EEG measures have been related to various (neurocognitive) states and processes, such as (i) cognitive decline in Alzheimer's disease and mild cognitive impairment (Cassani, Estarellas, San-Martin, Fraga & Falk, Reference Cassani, Estarellas, San-Martin, Fraga and Falk2018; Meghdadi, Stevanović Karić, McConnell, Rupp, Richard, Hamilton, Salat & Berka, Reference Meghdadi, Stevanović Karić, McConnell, Rupp, Richard, Hamilton, Salat and Berka2021; Stam, Reference Stam2005), (ii) abnormalities in autism disorders (Murias, Webb, Greenson & Dawson, Reference Murias, Webb, Greenson and Dawson2007; Heunis, Aldrich, Peters, Jeste, Sahin, Scheffer & De Vries, Reference Heunis, Aldrich, Peters, Jeste, Sahin, Scheffer and De Vries2018; Wang Li, Li, Li, Han & Wan, Reference Wang, Li, Li, Li, Han and Wan2013), (iii) schizophrenia (Kam, Bolbecker, O'Donnell, Hetrick & Brenner, Reference Kam, Bolbecker, O'Donnell, Hetrick and Brenner2013; Sponheim, Clementz, Iacono & Beiser, Reference Sponheim, Clementz, Iacono and Beiser2000), (iv) memory consolidation (Brokaw, Tishler, Manceor, Hamilton, Gaulden, Parr & Wamsley, Reference Brokaw, Tishler, Manceor, Hamilton, Gaulden, Parr and Wamsley2016; Jabès, Klencklen, Ruggeri, Michel, Lavenex & Lavenex, Reference Jabès, Klencklen, Ruggeri, Michel, Lavenex and Lavenex2021), and, critically for our discussion herein, (v) (multilingual) language learning, processing and use (Bice, Yamasaki & Prat, Reference Bice, Yamasaki and Prat2020; Kliesch, Giroud & Meyer, Reference Kliesch, Giroud and Meyer2020; Pereira Soares, Kubota, Rossi & Rothman, Reference Pereira Soares, Kubota, Rossi and Rothman2021; Prat, Yamasaki, Kluender & Stocco, Reference Prat, Yamasaki, Kluender and Stocco2016; Prat, Yamasaki & Peterson, Reference Prat, Yamasaki and Peterson2019; Prat, Madhyastha, Mottarella & Kuo, Reference Prat, Madhyastha, Mottarella and Kuo2020).

Figure 3. Time-frequency representation of a single electrode (upper panel) and its topographical distribution averaged for the time-frequency windows of interest (lower panel). Color in the plots indicates relative power change.

The utility of brain oscillatory analysis applying to language science is now well established (see Prystauka & Lewis, Reference Prystauka and Lewis2019 for discussion). While it offers significant opportunities to better understand links between theories and brain mechanisms (Buzsáki & Watson, Reference Buzsáki and Watson2012; Mazaheri, Segaert, Olichney, Yang, Niu, Shapiro & Bowman, Reference Mazaheri, Segaert, Olichney, Yang, Niu, Shapiro and Bowman2018; Newson & Thiagarajan, Reference Newson and Thiagarajan2019), there are important considerations to keep in mind before one seeks to adopt the method to specific domains, such as bilingualism. In this context, recent advances in neural computational modelling have highlighted some recurrent issues, including the exclusion/neglect of basic methodological assumptions of neural oscillations (see Donoghue, Schaworonkow & Voytek, Reference Donoghue, Schaworonkow and Voytek2021 for an overview). Failing to herald these concerns increases the risk of false interpretations and inconsistency of results. To offer just one example, many studies looking at brainwaves assume that oscillatory activity and its related power is omnipresent in EEG data. This assumption is not always true. Power in the EEG signal is also present in the form of aperiodic (1/f) activity (or by some initially called ‘background noise’ – Podvalny, Noy, Harel, Bickel, Chechik, Schroeder, Mehta, Tsodyks & Malach, Reference Podvalny, Noy, Harel, Bickel, Chechik, Schroeder, Mehta, Tsodyks and Malach2015; Voytek, Kramer, Case, Lepage, Tempesta, Knight & Gazzaley, Reference Voytek, Kramer, Case, Lepage, Tempesta, Knight and Gazzaley2015), whereby power decreases exponentially as a function of frequency (f) (Donoghue, Haller, Peterson, Varma, Sebastian, Gao, Noto, Lara, Wallis, Knight, Shestyuk & Voytek, Reference Donoghue, Haller, Peterson, Varma, Sebastian, Gao, Noto, Lara, Wallis, Knight, Shestyuk and Voytek2020; Freeman, Burke & Holmes, Reference Freeman, Burke and Holmes2003; He, Reference He2014). As a result, it cannot be a priori assumed that all apparent sources of power changes are universally brainwaves, since the power obtained by spectral analysis can also (or solely) derive from aperiodic activity (e.g., Cross, Corcoran, Schlesewsky, Kohler & Bornkessel-Schlesewsky, Reference Cross, Corcoran, Schlesewsky, Kohler and Bornkessel-Schlesewsky2020; Immink, Cross, Chatburn, Baumeister, Schlesewsky & Bornkessel-Schlesewsky (2021) for studies relating 1/f to meaningful individual differences in language and visuomotor/learning domains).

Unpacking the EEG signal

Although brain oscillations were first observed more than 100 years ago, with the advent of better recording devices and analysis techniques in the 1960s, researchers moved quickly from studying oscillatory phenomena to analyzing Event Related Potentials (ERPs), i.e., the EEG signal analyzed in the time domain and locked to task stimuli. The motivation behind this analytical shift likely relates to the very nature of the EEG signal. In fact, the EEG signal recorded during the execution of a cognitive task is composed of ‘meaningful information’ or ‘signal’ (cognitive processes) and ‘noise’ (background EEG information). Averaging the response signal across many trials from the same experimental condition increases the signal to noise ratio: significantly reducing the noise (assumed to be randomly distributed) and preserving the signal (assumed to be stationary) (Handy, Reference Handy2005; Luck, Reference Luck, Luck and Kappenman2012). It would be reasonable to say that within EEG applications in psycho-neurolinguistics research, ERP studies have dominated the landscape over the past few decades (Luck & Kappenman, Reference Luck and Kappenman2011; Steinhauer, Connolly, Stemmer & Whitaker, Reference Steinhauer, Connolly, Stemmer, Whitaker and Whitaker2008). There is no doubt that ERP research has provided significant foundational discoveries about both the time course and the neural basis of various underlying cognitive processes, including those involved in or conditioned by language.

In recent years however, interest in the spectral content of the EEG signal has resurged. This renewed interest is borne of the reality that much information inevitably gets lost when the EEG signal is averaged in the time domain. This is especially the case when related to coupling and decoupling (i.e., the dynamic segregation of brain circuits into transiently reconfigured functional networks) mechanisms of neural pools involved in cognitive processes (e.g., Varela et al., Reference Varela, Lachaux, Rodriguez and Martinerie2001). Interestingly, the upsurge of studies looking at brainwaves parallels the development and incremental use of modern (f)MRI imaging techniques that focus on network dynamics and functional connectivity (Sulpizio, Del Maschio, Fedeli & Abutalebi, Reference Sulpizio, Del Maschio, Fedeli and Abutalebi2020). This parallel development demonstrates the growing perception in cognitive neuroscience that functional network dynamics sit at the core of human cognition (Fell, Reference Fell2007; Luck & Kappenman, Reference Luck and Kappenman2011).

When examining the EEG response as a function of an experimental event, one can observe two different types of oscillatory activity changes: phase-locked (evoked) and non-phase-locked (induced). Whereas time-locked oscillatory activity is intrinsically bound to an event, the same is not (always) true for non-phase-locked activity. This is the case because oscillations exist in the absence of external stimuli and their phase varies at the onset of the events throughout the experiment. Averaging across trials will reduce, if not cancel, non-phase-locked responses (see Figure 4).

Figure 4. Demonstration of phase-locked (evoked) and non-phase-locked (induced) activity in the simulated EEG time series (on the left) and in the time-frequency domain (on the right), both in individual trials (the upper three panels) and in the average response (the bottom panel). The plots in the bottom panel demonstrate how the phase-locked response is preserved in both the ERP and the average TFR responses, and how the non-phase-locked response is cancelled out in the ERP but is preserved in the average TFR. Adapted from Bastiaansen et al. (2011), implemented based on the code from Cohen, 2014.

The non-phase-locked signal is particularly informative when relating to patterns of synchronization and desynchronization of neuronal activity across neural networks/pools. Evidence over the past few decades has revealed that neural (de)synchronization strictly relates to coupling and decoupling of functional networks in the brain (e.g., Pfurtscheller & Berghold, Reference Pfurtscheller and Berghold1989; Singer, Reference Singer1999; Varela et al., Reference Varela, Lachaux, Rodriguez and Martinerie2001). Underlying this claim is the observation that synchronous, repetitive neuronal firing facilitates the activation of functional brain networks, which, in turn, increases the likelihood of neuronal entrainment and subsequent synchronous firing (e.g., König & Schillen, Reference König and Schillen1991). Additionally, elements belonging to the same functional network tend to fire synchronously at any given frequency. This frequency specificity permits neuronal pools to participate at different times in different representations. Thus, the synchronous oscillatory activity in many frequencies plays a crucial role in connecting areas that belong to the same functional network. More importantly, not only does oscillatory neuronal synchrony serve the purpose of recruiting all the relevant elements in a network, it also binds together the information within these different elements (Gray, König, Engel & Singer, Reference Gray, König, Engel and Singer1989). Given that a base level of synchrony in neural firing is constantly present, oscillatory activity dominates raw EEG data.

To sum up, experimental stimuli/events occur at random phases of the ongoing oscillatory cycle and will modulate these oscillations, giving rise, therefore, to non-phase-locked responses. That is, different from phase-locked responses (ERPs), non-phase-locked ones mostly reflect synchronization of underlying neuronal and network activity. Since (de)synchronization activity is a proxy for coupling and decoupling of functional networks, it follows that non-phase-locked oscillatory EEG responses provide researchers with a window into functional (network) brain dynamics, which is especially useful to researchers working within the cognitive neuroscience of bi-/multilingualism.

Furthermore, for researchers who primarily use ERPs to study linguistic processing, it is useful to highlight both the overlap (where the two index the same thing) and improved complementarity of TFRs to ERPs (where TFR provides extra information to what ERPs can show). For example, going back to one of the classic ERP components found in psycholinguistic studies, the N400, applying a TFR approach to the ERP has been shown to manifest as delta and theta power increase (Roehm, Schlesewsky, Bornkessel, Frisch & Haider, Reference Roehm, Schlesewsky, Bornkessel, Frisch and Haider2004; Steele, Bernat, Van Den Broek, Collins, Patrick & Marsolek, Reference Steele, Bernat, Van Den Broek, Collins, Patrick and Marsolek2013). Additionally, there is research suggesting the relationship between N400 and beta power (Wang, Jensen, Van den Brink, Weder, Schoffelen, Magyari, Hagorrt & Bastiaansen, Reference Wang, Jensen, Van den Brink, Weder, Schoffelen, Magyari, Hagorrt and Bastiaansen2012), however, the results are mixed (Lewis, Schoffelen, Hoffmann, Bastiaansen & Schriefers, Reference Lewis, Schoffelen, Hoffmann, Bastiaansen and Schriefers2017). In this respect, further scrutinizing the relationship between ERPs and TFR components will move the field of cognitive neuroscience of language and bilingualism closer to understanding the neural mechanisms underlying these common EEG signatures. Given the (partial) translateability, one does not have to sacrifice the information provided by ERPs if a TFR approach is adopted. In the case that both are applied, they can provide a confirmatory approach – that is, adopting both and probing for overlap (e.g., that the N400, in fact, translates to power in the delta, theta or beta domain(s)) can improve the ecological validity of what one or the other show in isolation. However, ERPs are incapable of providing information regarding induced power (see Figure 4). Induced power refers to neural activity modulated by an event of interest, yet not phase-locked to it. This potentially occurs more prevalently than what one might expect given that research has shown that the phase of an oscillation induced by an event can vary significantly across trials (Donoghue et al., Reference Donoghue, Schaworonkow and Voytek2021). This is important to bear in mind as it means that some significant information could be missed by an ERP approach. In the case of TFRs, increased granularity and relevant sensitivity is to be expected.With an eye specifically on application of oscillatory dynamics in bilingual research, what does it bring to the table above and beyond what ERPs have already told us? First, its augmented sensitivity means that it is able to capture signal changes that vary in time and phase across trials and participants (Kielar, Meltzer, Moreno, Alain & Bialystok, Reference Kielar, Meltzer, Moreno, Alain and Bialystok2014). As Kielar and colleagues (Reference Kielar, Meltzer, Moreno, Alain and Bialystok2014) point out, when the observed ERP effect is qualitatively different (as observed in non-native language processing, especially at lower levels of L2 proficiency e.g., Foucart & Frenck-Mestre, Reference Foucart and Frenck-Mestre2012; Morgan-Short, Steinhauer, Sanz & Ullman, Reference Morgan-Short, Steinhauer, Sanz and Ullman2012; Martin, Thierry, Kuipers, Boutonnet, Foucart & Costa, Reference Martin, Thierry, Kuipers, Boutonnet, Foucart and Costa2013; Gabriele, Fiorentino & Alemán-Bañón, Reference Gabriele, Fiorentino and Alemán-Bañón2013; Alemán-Bañón, Fiorentino & Gabriele, Reference Alemán-Bañón, Fiorentino and Gabriele2018), strong conclusions are often made. For example, it has been claimed on the basis of such evidence that acquisition/processing is fundamentally different between natives and non-natives (Foucart & Frenck-Mestre, Reference Foucart and Frenck-Mestre2012). In actuality, it is spurious to make such sweeping conclusions as differences might rather reflect reduced neural activity or greater phase variability in a particular population. Instead, looking at the power of the induced (non-phase-locked) activity can help discriminate between competing interpretations.

Second, introducing a(n additional) frequency dimension to the analysis of the EEG signal multiplies the number of ways we can look at the data and enables us to ask new –different – questions ERPs alone cannot address. Let us consider a few examples here. First, one can use brainwave analysis to examine changes in rs-EEG as a function of bilingual experience – that is, (how) does (the degree of) dual language exposure and usage patterns affect cumulative neural change in the bilingual brain in the short and long terms (Luk, Pliatsikas & Rossi, Reference Luk, Pliatsikas and Rossi2020; Pereira Soares et al., Reference Pereira Soares, Kubota, Rossi and Rothman2021). Second, frequency analysis permits the exploration of the role exponents of bilingualism (e.g., different language use profiles) play in how brain oscillations might differentially synchronize with physical (e.g., syllable rate) and abstract (e.g., syntactic structure) linguistic units (Blanco-Elorrieta, Ding, Pylkkänen & Poeppel, Reference Blanco-Elorrieta, Ding, Pylkkänen and Poeppel2019). Given that neural oscillations have been extensively studied in broader fields of cognitive neuroscience for a considerably long time, bringing them to the bilingualism domain opens up several possibilities. TFRs are, thus, poised to improve interpretations of apparent bilingual effects on linguistic processing, brain and cognition in the context of the broader neuroscience literatures on memory, attention, cognitive control, motivation, etc.

Using brain oscillations to examine language processing: general findings and new applications to bilingualism

The contemporary upsurging trend of analyzing neural oscillations in language processing is fairly recent (Meyer, Reference Meyer2018; Prystauka & Lewis, Reference Prystauka and Lewis2019). This work has revealed important insights for investigating the underlying domain-general and language-specific subcomponents of linguistic processing, from the lower levels (e.g., segmentation and identification of phonological units) to higher levels of information (e.g., comprehension of lexical, semantic, and syntactic information). Despite some variability, research shows relatively consistent patterns of oscillatory activity for different aspects of language processing. For example, theta oscillations have been linked to lexical-semantic retrieval (Bastiaansen & Hagoort, Reference Bastiaansen, Van Berkum and Hagoort2003), while beta and gamma oscillations have been connected to syntactic and semantic structure building, respectively (Bastiaansen & Hagoort, Reference Bastiaansen and Hagoort2006; see Prystauka & Lewis, Reference Prystauka and Lewis2019 for a more comprehensive review). However, theta, beta and gamma have also been associated with a multitude of domain general cognitive processes. To name a few, theta power with a frontocentral topography has been associated with cognitive control (Cavanagh & Frank, Reference Cavanagh and Frank2014) and short-term memory (Hsieh & Ranganath, Reference Hsieh and Ranganath2014). Alpha oscillations are thought to play a role in inhibiting task-irrelevant information by means of ‘gating by inhibition’ (Jensen & Mazaheri, Reference Jensen and Mazaheri2010) and ‘inhibition-timing’ (Klimesch, Sauseng & Hanslmayr, Reference Klimesch, Sauseng and Hanslmayr2007). Beta oscillations have been proposed to reflect maintenance of the current cognitive set (Engel & Fries, Reference Engel and Fries2010) and the top-down propagation of predictions to lower levels of processing. Moreover, gamma oscillations might reflect the matching of top-down prediction and bottom-up input processing (Herrmann, Munk & Engel, Reference Herrmann, Munk and Engel2004; Bastos, Usrey, Adams, Mangun, Fries & Friston, Reference Bastos, Usrey, Adams, Mangun, Fries and Friston2012).

One of the ways in which neural oscillations have been used to study language processing has been to investigate the phenomenon of entrainment (phase-resetting and alignment) of neural oscillations to discrete phonological units in the speech stream (Luo & Poeppel, Reference Luo and Poeppel2007; Giraud & Poeppel, Reference Giraud and Poeppel2012). Such entrainment has been speculated to support the identification of linguistically meaningful segments (phonemes, syllables and phrases), used by higher-level processing mechanisms for syntactic and semantic structure-building. Previous research suggests that gamma oscillations track the phonemic rate of speech, theta oscillations are modulated by the syllabic structure, and delta oscillations are sensitive to phrase boundaries (for review, see Kösem & van Wassenhove, Reference Kösem and van Wassenhove2017), phase-locking of neural oscillations to the phase of external physical stimuli such as speech – and intrinsic synchronization reflecting the generation of predictions of abstract linguistic units such as morphemes and words (see Meyer, Sun & Martin, Reference Meyer, Sun and Martin2020; Meyer, Grigutsch, Schmuck, Gaston & Friederici, Reference Meyer, Grigutsch, Schmuck, Gaston and Friederici2015; and related commentaries). At higher levels of language processing, neural oscillations have been used to study: (i) semantic and syntactic structure building, including scenarios where such structure building is disrupted by semantic and syntactic anomalies (Bastiaansen, Magyari & Hagoort, Reference Bastiaansen, Magyari and Hagoort2010; Davidson & Indefrey, Reference Davidson and Indefrey2007; Lewis, Schoffelen, Schriefers & Bastiaansen, Reference Lewis, Schoffelen, Schriefers and Bastiaansen2016; Kielar et al., Reference Kielar, Meltzer, Moreno, Alain and Bialystok2014; Kielar, Panamsky, Links & Meltzer, Reference Kielar, Panamsky, Links and Meltzer2015), (ii) anticipatory (Piai, Anderson, Lin, Dewar, Parvizi, Dronkers & Knight, Reference Piai, Anderson, Lin, Dewar, Parvizi, Dronkers and Knight2016; Rommers, Dickson, Norton, Wlotko & Federmeier, Reference Rommers, Dickson, Norton, Wlotko and Federmeier2017) and referential processing (Meyer et al., Reference Meyer, Grigutsch, Schmuck, Gaston and Friederici2015; Nieuwland & Martin, Reference Nieuwland and Martin2017), (iii) situationally dependent and nonliteral language (Akimoto, Takahashi, Gunji, Kaneko, Asano, Matsuo, Ota, Kunugi, Hanakawa, Mazuka & Kamio, Reference Akimoto, Takahashi, Gunji, Kaneko, Asano, Matsuo, Ota, Kunugi, Hanakawa, Mazuka and Kamio2017; Canal, Pesciarelli, Vespignani, Molinaro & Cacciari, Reference Canal, Pesciarelli, Vespignani, Molinaro and Cacciari2017) and (iv) working memory pertaining to sentence level meaning comprehension (Meltzer & Braun, Reference Meltzer and Braun2011; Vassileiou, Meyer, Beese & Friederici, Reference Vassileiou, Meyer, Beese and Friederici2018; Rommers & Federmeier, Reference Rommers and Federmeier2018) as well as the role of the sensorimotor networks in language comprehension (Lam, Bastiaansen, Dijkstra & Rueschemeyer, Reference Lam, Bastiaansen, Dijkstra and Rueschemeyer2017; Moreno, de Vega & León, Reference Moreno, de Vega and León2013; Moreno, de Vega, León, Bastiaansen, Lewis & Magyari, Reference Moreno, de Vega, León, Bastiaansen, Lewis and Magyari2015).

Despite the utility of neural oscillations within the native-language processing literature, its application in bilingualism remains a relatively uncharted territory. In our view, this is a missed opportunity. As framed in what follows, experimental paradigms utilizing oscillations have the potential to reveal, if not eventually disentangle, the relative roles/contributory weightings of cognitive and linguistic mechanisms recruited during bilingual language processing. To date, only a few bilingual studies have sought to capitalize on the (time-)frequency dimension of electrophysiological signals. Starting with the lower levels of language processing, Blanco-Elorrieta et al. (Reference Blanco-Elorrieta, Ding, Pylkkänen and Poeppel2019) used MEG to examine syllable and phrase tracking by bilingual speakers with varying degrees of language proficiency under different noise conditions. They found that syllable tracking, as reflected in theta power, was affected by noise equally in Mandarin-natives with various degrees of English L2 proficiency. In contrast, phrase tracking (reflected in delta power) was modulated by both noise strength and language proficiency, such that with increasing proficiency L2 speakers were better able to track phrases under more severe noise conditions. These results suggest that processing an L2 might differ from processing one's native language, starting already at lower levels of parsing out the incoming speech stream into its building blocks (McCauley, Isbilen & Christiansen, Reference McCauley, Isbilen and Christiansen2017). Segmentation difficulties at this level could then have upstream effects for higher-level semantic and syntactic processing, explaining differences observed among speakers with different levels of proficiency. This work highlights how examining the frequency domain can open up new avenues for exploring the interaction between low- and high-level processing. In particular, it speaks to how this interaction potentially changes as a function of language experience and use, a topic with significant implications for bilingual sentence processing (and any neurocognitive dual language effects) more generally. For example, this could mean that some evidence taken to reflect bona fide or apparent patterns of shallow L2 processing (Clahsen & Felser, Reference Clahsen and Felser2006; Reference Clahsen and Felser2018) at a higher level might begin at a much lower level, at least under noise in the lab (if not the noise of the real world).

We should bear in mind that the Blanco-Elorrieta et al. (Reference Blanco-Elorrieta, Ding, Pylkkänen and Poeppel2019) study involves entrainment. Entrainment is conceptually distinct from other relevant approaches using brain oscillations of equal importance and contextual appropriateness for examining (bilingual) language processing. As mentioned above, entrainment examines synchronization of neural oscillations to the rhythmic properties of the external signal (i.e., speech). However, an approach more suited for examining brain activation as a function of more abstract linguistic manipulations is the analysis of power fluctuations over time.

While the linguistic and the cognitive bases of bilingual language acquisition/processing and coactivation have been extensively studied using behavioral, ERP, and other neuroimaging methods (Tolentino & Tokowicz, Reference Tolentino and Tokowicz2011; DeLuca, Miller, Swanson & Rothman, Reference DeLuca, Miller, Swanson, Rothman, Morgan-Short and van Hellto appear; DeLuca, Miller, Pliatsikas & Rothman, Reference DeLuca, Miller, Pliatsikas, Rothman and Schwieter2019a for reviews), only a few bilingual studies have used neural oscillations. A particularly illustrative example is the study by Bakker, Takashima, Van Hell, Janzen and McQueen (Reference Bakker, Takashima, Van Hell, Janzen and McQueen2015) investigating changes in oscillatory dynamics during novel word learning. Participants were exposed to new words during a learning phase that were designed to phonologically compete with existing English words (i.e., cathedruk, competing with the existing word cathedral). In the recall phase, participants were exposed to the newly learned words plus novel ones. It was predicted that if the new words are encoded and consolidated in memory they should compete for selection with existing (English) lexical items, as signaled by higher RTs during the memory recall phase (tested 24 hours later). As hypothesized, newly learned words showed longer RTs than unfamiliar, completely novel words, suggesting that the former started to compete for selection with existing lexical items. Crucially, oscillation results revealed an increase in theta frequencies (4-8 Hz) for the newly learned words, interpreted as theta-band oscillations playing a role in lexicalization and memory encoding.

The results of Bakker et al. (Reference Bakker, Takashima, Van Hell, Janzen and McQueen2015) were supported by a follow-up study using MEG (Bakker-Marshall, Takashima, Schoffelen, Van Hell, Janzen & McQueen, Reference Bakker-Marshall, Takashima, Schoffelen, Van Hell, Janzen and McQueen2018), demonstrating a modulation of theta after novel word consolidation and connecting the source of the oscillation to the left posterior middle temporal gyrus (pMTG), which is involved in lexical storage (see Nozari & Pinet, Reference Nozari and Pinet2020 for a review). In line with their previous results, the authors concluded that theta synchronization may enable lexical access by activating the distributed semantic, phonological, and orthographic representations of the newly learned words.

The general finding that theta is connected to memory consolidation is supported by other data (Lisman, Reference Lisman2010; Lisman & Jensen, Reference Lisman and Jensen2013), including a study on word translation in English–German bilinguals (Grabner, Brunner, Leeb, Neuper & Pfurtscheller, Reference Grabner, Brunner, Leeb, Neuper and Pfurtscheller2007). Grabner et al. (Reference Grabner, Brunner, Leeb, Neuper and Pfurtscheller2007) showed modulations in theta specifically for higher frequency words, which could reflect a greater role of memory for lexical items that have larger semantic networks. These results are similar to those of Bastiaansen, Van Berkum and Hagoort (Reference Bastiaansen, Van Berkum and Hagoort2003), for which increased theta for semantically rich versus semantically impoverished words in monolingual speakers was observed. This was argued to suggest that theta increase may be reflective of the “activation” of larger semantic networks. As discussed below, beyond the single word level, modulations of theta power and coherence (together with alpha, beta and gamma) have been proposed to reflect the coordination of working memory and the selection/retrieval of networks during sentence processing (Bastiaansen & Hagoort, Reference Bastiaansen and Hagoort2006; Hagoort, Reference Hagoort2005), and the overall engagement of working memory resources (see Meyer, Reference Meyer2018 for a review).

At the sentence level, Kielar et al. (Reference Kielar, Meltzer, Moreno, Alain and Bialystok2014) conducted one of the first studies on differences in oscillatory patterns during sentence processing between bilinguals and monolinguals, reanalyzing a dataset from Moreno, Bialystok, Wodniecka and Alain (Reference Moreno, Bialystok, Wodniecka and Alain2010). The original EEG data was collected for an ERP study comparing sentences that were grammatical (and felicitous) with some that were semantically infelicitous or syntactically incorrect. Kielar et al. (Reference Kielar, Meltzer, Moreno, Alain and Bialystok2014) revealed decreases in alpha-beta frequencies across the monolingual and bilingual groups for both semantic and syntactic violations. Moreover, delta and theta increases were observed only for the semantic violation condition. Of note, bilinguals had a smaller decrease in alpha and beta bands to syntactic violations compared to monolinguals, which was interpreted as reflecting more efficiency for bilinguals in the face of equal behavioral performance.

Rossi and Prystauka (Reference Rossi and Prystauka2019) investigated the time-course of oscillatory activity during the processing of Spanish gender and number (expressed via agreement on clitic pronouns) in native speakers of Spanish and in English second language (L2) learners of Spanish. The results demonstrated that both native and L2 speakers showed a decrease in alpha and beta bands when agreement on the clitic was violated. Critically, the two groups differed in the duration of the observed effect, such that the oscillatory effect in Spanish native speakers lasted longer than the one observed in L2 learners. This difference was explained in the context of the updated theory of Hebb's cell assembly framework (Hebb, Reference Hebb1949), whereby cell assemblies have two distinct phases of activation: a fast ignition phase and a reverberation phase. The authors hypothesize that differences in processing would manifest in the reverberation phase as decreased duration of the oscillatory response to morpho-syntactic violations in bilinguals relative to monolinguals. In light of previous work showing that syntactic processing can be constrained by working memory (WM) (Vos, Gunter, Kolk & Mulder, Reference Vos, Gunter, Kolk and Mulder2001; Coughlin & Tremblay, Reference Coughlin and Tremblay2011), one explanation could be that the reverberation phase is decreased in bilinguals because of the higher working memory load due to competing information from their coactivated second language (Kroll, Bobb & Wodniecka, Reference Kroll, Bobb and Wodniecka2006; Blumenfeld & Marian, Reference Blumenfeld and Marian2007).

Using oscillations as a window to understand bilingual language control

A hallmark finding of neurocognitive bilingualism research is the pervasive, simultaneous co-activation of the two languages (Blumenfeld & Marian, Reference Blumenfeld and Marian2013; Green, Reference Green1998; Hoshino & Thierry, Reference Hoshino and Thierry2011), even at the earliest/lowest stages of proficiency. In fact, relevant ERP studies have shown that L2 learning can be rapid and efficient (Bakker et al., 2014, Reference Bakker, Takashima, Van Hell, Janzen and McQueen2015; Morgan-Short et al., Reference Morgan-Short, Steinhauer, Sanz and Ullman2012), even in the absence of evidence in behavioral performance (McLaughlin, Osterhout & Kim, Reference McLaughlin, Osterhout and Kim2004; Osterhout, Poliakov, Inoue, McLaughlin, Valentine, Pitkanen, Frenck-Mestre & Hirschensohn, Reference Osterhout, Poliakov, Inoue, McLaughlin, Valentine, Pitkanen, Frenck-Mestre and Hirschensohn2008; Tokowicz & MacWhinney, Reference Tokowicz and MacWhinney2005). For example, speakers can learn and consolidate new words in a novel language in the span of only 24-48 hours. Crucially, those new words immediately begin to compete for selection with the native language, revealing the plasticity of the linguistic system (Bakker et al., Reference Bakker, Takashima, Van Hell, Janzen and McQueen2015).

Moreover, given the ample evidence of coactivation in all instances of bilingual language acquisition, one of the key questions that has driven neurocognitive bilingual research in general has been to understand the behavioral and neural mechanisms that enable bilingual speakers to manage dual language competition. Simultaneous language coactivation requires a mechanism of control to manage contextually appropriate language selection. Indeed, managing and resolving competition is vital for target language selection, a crucial and necessary aspect for successful speech production and comprehension (Kroll, Dussias, Bogulski & Valdés-Kroff, Reference Kroll, Dussias, Bogulski, Valdés-Kroff and Ross2012). Early models of bilingual language processing proposed that the relevant mechanism is domain-general cognitive control (e.g., the Inhibitory Control Model, Green, Reference Green1998; Levy, McVeigh, Marful & Anderson, Reference Levy, McVeigh, Marful and Anderson2007; Linck, Kroll & Sunderman, Reference Linck, Kroll and Sunderman2009; Philipp, Gade & Koch, Reference Philipp, Gade and Koch2007). The proposal that inhibition (potentially attention more broadly, see Bialystok & Craik, Reference Bialystok and Craik2022) is at the center of successful bilingual language control is supported by a significant neuroimaging literature. Available work is largely consistent with the claim that continuous juggling of two (or more) languages in the brain engages neural networks involved in domain general cognitive control, especially those that overlap with language control. Research has consistently shown that dual language use, at least past a certain threshold of experience, impacts cortical (Abutalebi, Della Rosa, Green, Hernandez, Scifo, Keim, Cappa & Costa, Reference Abutalebi, Della Rosa, Green, Hernandez, Scifo, Keim, Cappa and Costa2012; Mechelli, Crinion, Noppeney, O'Doherty, Ashburner, Frackowiak & Price, Reference Mechelli, Crinion, Noppeney, O'Doherty, Ashburner, Frackowiak and Price2004; Pereira Soares, Ong, Abutalebi, Del Maschio, Sewell & Weekes, Reference Pereira Soares, Ong, Abutalebi, Del Maschio, Sewell and Weekes2019; Pliatsikas, Johnstone & Marinis, Reference Pliatsikas, Johnstone and Marinis2014; Stein, Federspiel, Koenig, Wirth, Strik, Wiest, Brandeis & Dierks, Reference Stein, Federspiel, Koenig, Wirth, Strik, Wiest, Brandeis and Dierks2012) and subcortical/basal ganglia (Burgaleta, Sanjuán, Ventura-Campos, Sebastian-Galles & Ávila, Reference Burgaleta, Sanjuán, Ventura-Campos, Sebastian-Galles and Ávila2016; Pliatsikas, DeLuca, Moschopoulou & Saddy, Reference Pliatsikas, DeLuca, Moschopoulou and Saddy2017; Zou, Ding, Abutalebi, Shu & Peng, Reference Zou, Ding, Abutalebi, Shu and Peng2012), white matter tracts (Kuhl, Stevenson, Corrigan, van den Bosch, Can & Richards, Reference Kuhl, Stevenson, Corrigan, van den Bosch, Can and Richards2016; Mohades, Van Schuerbeek, Rosseel, Van De Craen, Luypaert & Baeken, Reference Mohades, Van Schuerbeek, Rosseel, ., Van De Craen, Luypaert and Baeken2015; Rossi, Cheng, Kroll, Diaz & Newman, Reference Rossi, Cheng, Kroll, Diaz and Newman2017), functional adaptations (Luk, Anderson, Craik, Grady & Bialystok, Reference Luk, Anderson, Craik, Grady and Bialystok2010; Olulade, Jamal, Koo, Perfetti, LaSasso & Eden, Reference Olulade, Jamal, Koo, Perfetti, LaSasso and Eden2015; Rossi, Newman, Kroll & Diaz, Reference Rossi, Newman, Kroll and Diaz2018), resting state functional connectivity (e.g., Grady, Luk, Craik & Bialystok, Reference Grady, Luk, Craik and Bialystok2015; Luk, Green, Abutalebi & Grady, Reference Luk, Green, Abutalebi and Grady2012; Rodríguez-Pujadas, Sanjuán, Ventura-Campos, Román, Martin, Barceló, Costa & Ávila, Reference Rodríguez-Pujadas, Sanjuán, Ventura-Campos, Román, Martin, Barceló, Costa and Ávila2013) and even the chemical composition of the brain (Pliatsikas, Pereira Soares, Voits, DeLuca & Rothman, Reference Pliatsikas, Pereira Soares, Voits, DeLuca and Rothman2021; Weekes, Abutalebi, Mak, Borsa, Pereira Soares, Chiu & Zhang, Reference Weekes, Abutalebi, Mak, Borsa, Pereira Soares, Chiu and Zhang2018). These neuroimaging findings have been fairly consistent across studies (see Pliatsikas, Reference Pliatsikas2020; Zhang, Wu & Thierry, Reference Zhang, Wu and Thierry2020 for recent reviews).

More recently, studies have turned to address the complex and dynamic nature of bilingualism as a set of individual difference factors proxying for the relative opportunities and intensity one has with engaging dual language control. In other words, bilingualism and any potentially ensuing effects are viewed as spectral (continuous variables) as opposed to absolute (e.g., Luk & Bialystok, Reference Luk and Bialystok2013; Li, Abutalebi, Zou, Yan, Liu, Feng, Wang, Guo & Ding, Reference Li, Abutalebi, Zou, Yan, Liu, Feng, Wang, Guo and Ding2015; Beatty-Martínez & Dussias, Reference Beatty-Martínez and Dussias2019; DeLuca, Rothman, Bialystok & Pliatsikas, Reference DeLuca, Rothman, Bialystok and Pliatsikas2019b; DeLuca, Segaert, Mazaheri & Krott, Reference DeLuca, Segaert, Mazaheri and Krott2020; Gullifer, Chai, Whitford, Pivneva, Baum, Klein & Titone, Reference Gullifer, Chai, Whitford, Pivneva, Baum, Klein and Titone2018; Kuhl et al., Reference Kuhl, Stevenson, Corrigan, van den Bosch, Can and Richards2016; Sulpizio et al., Reference Sulpizio, Del Maschio, Fedeli and Abutalebi2020). With the above as a backdrop, the question of how EEG analyses have contributed and/or should contribute to this general program comes to the fore. In many ways, EEG is ideal for studying similar things that much of the MRI literature reviewed in brief above has showed. While EEG and MRI as methods have (some) functional overlap, making EEG a complementary (also a more time and cost effective approach) tool to MRI, there are things that EEG is potentially better positioned to address. Moreover, EEG predictions and their correspondences to those of (f)MRI stemming from models of bilingual neurocognitive adaptations – for example, the Adaptive Control Hypothesis (ACH, Abutalebi & Green, Reference Abutalebi and Green2016) and the Bilingual Anterior to Posterior and Subcortical Shift model (BAPSS, Grundy, Anderson & Bialystok, Reference Grundy, Anderson and Bialystok2017) – have recently been offered by the Unifying Bilingual Experience Trajectory model (UBET, DeLuca et al., Reference DeLuca, Segaert, Mazaheri and Krott2020). And yet, comparatively few studies have opted to use EEG over MRI.

Recall that EEG allows one to capture processes as they unfold in real time and, thus, not observable with MRI (a tool primarily concerned with a final observable outcome). Several early ERP components have been identified that relate to the increased effort required while performing demanding cognitive tasks: (i) the fronto-central N200 and fronto-central/parietal P300, which have mostly been associated with information updating, attention and inhibition of processing costs (e.g., Groom & Cragg, Reference Groom and Cragg2015; Heil, Osman, Wiegelmann, Rolke & Hennighausen, Reference Heil, Osman, Wiegelmann, Rolke and Hennighausen2000), (ii) the Error Related Negativity (ERN), which indexes conflict monitoring (e.g., Botvinick, Braver, Barch, Carter & Cohen, Reference Botvinick, Braver, Barch, Carter and Cohen2001; Gabrys, Tabri, Anisman & Matheson, Reference Gabrys, Tabri, Anisman and Matheson2018) and (iii) the N450, which reflects both conflict monitoring and inhibition (Larson, Kaplan, Kaushanskaya & Weismer, Reference Larson, Kaplan, Kaushanskaya and Weismer2020; Zahedi, Abdel Rahman, Stürmer & Sommer, Reference Zahedi, Abdel Rahman, Stürmer and Sommer2019) (see Cespón & Carreiras, Reference Cespón and Carreiras2020 for a review on bilingualism, cognition and ERPs). Findings so far in this literature are mixed with regard to bilingual brain adaptations. Whereas some have found no differences between monolinguals and bilinguals (e.g., Fernández, Tartar, Padron & Acosta, Reference Fernández, Tartar, Padron and Acosta2013; Kousaie & Phillips, Reference Kousaie and Phillips2012; Moreno et al., Reference Moreno, de Vega, León, Bastiaansen, Lewis and Magyari2015), others have shown either only neurophysiological adaptations (e.g., Bice et al., Reference Bice, Yamasaki and Prat2020; Morrison, Kamal & Taler, Reference Morrison, Kamal and Taler2019), behavioral effects (e.g., Zunini, Morrison, Kousaie & Taler, Reference Zunini, Morrison, Kousaie and Taler2019; Morales, Yudes, Gómez-Ariza & Bajo, Reference Morales, Yudes, Gómez-Ariza and Bajo2015), or both (e.g., Barac, Moreno & Bialystok, Reference Barac, Moreno and Bialystok2016; Kousaie & Phillips, Reference Kousaie and Phillips2017).

While ERP studies in this domain are already scarce, there are even fewer studies analyzing neural oscillations. To the best of our knowledge, only four studies have been published to date. Given this small number, in what follows, we summarize in some detail the entirety of the available data. Inspired by work done on second language learning (Prat et al., Reference Prat, Yamasaki, Kluender and Stocco2016, Reference Prat, Yamasaki and Peterson2019), Bice et al. (Reference Bice, Yamasaki and Prat2020) investigated the effects of bilingual language use on the brain using rs-EEG correlates (both power and mean coherence). Results from a large sample of bilinguals (106) and monolinguals (91) revealed that bilinguals had increased alpha power and more pronounced and greater coherences in both alpha and beta frequency bands. Furthermore, data showed that alpha positively correlated with individual difference variables: i.e., more second language use, higher native-language proficiency, and earlier age of second-language acquisition. Similarly, beta power corresponded to non-native proficiency and theta to native-language proficiency (left-lateralized). These findings highlight the significant relationship between (the degree of) language use and the intrinsic plasticity of neurocognitive brain correlates. As such, these data add an important piece to the growing literature connecting neurocognitive adaptations and bilingualism.

Adopting a bilingual-centric approach where bilingualism is treated as a continuum regressed to experiential variables of dual language engagement and, thus, sidesteps the more traditional monolingual comparison (Luk & Bialystok, Reference Luk and Bialystok2013; DeLuca et al., Reference DeLuca, Miller, Pliatsikas, Rothman and Schwieter2019a, Reference DeLuca, Segaert, Mazaheri and Krott2020; Gullifer & Titone, Reference Gullifer and Titone2020), Pereira Soares et al. (Reference Pereira Soares, Kubota, Rossi and Rothman2021) collected rs-EEG data from a diversified pool of 106 bilinguals. Participants varied across several key dimensions, e.g., early versus late bilinguals (heritage (minority) language speakers and adult L2 learners), geographical location (Germany and Norway), age of onset, cumulative duration of bilingualism, social and home distribution of their language use and more). It was predicted that rs-EEG measures (power and functional connectivity) would vary across individuals as a function of degree of bilingual engagement. As predicted, results showed effects of AoA of bilingualism on high beta and gamma powers and greater usage of the non-societal language in and out of the home to modulate mean coherence indices in theta, alpha and gamma powers. These findings fortify the view of rs-EEG as a complementary measure for the neuroscience of bilingualism, and are consistent with claims that variability in dual-language engagement modulates observed neurocognitive effects.

Two recent studies using time frequency representation (TFR) analysis find complementary results. Calvo and Bialystok (Reference Calvo and Bialystok2021) tested bilingual and monolingual participants on a Proactive Interference Task in both verbal and nonverbal conditions while behavioral and EEG measures were recorded. Behavioral findings replicated related studies in the literature, showing faster RTs for bilinguals in comparison to monolinguals during interference trials, especially in the nonverbal condition. An ERP analysis revealed a larger N200 and decreased late positive component (LPC) amplitude for bilinguals in comparison to the matched monolingual group in the verbal condition. These findings were interpreted as bilinguals having more efficient task performance. Demonstrating the utility and complementarity of combining ERP and TFR, the TFR analysis showed differential patterns of results for the two groups. For the bilinguals, the nonverbal condition induced higher beta activity for interference trials, whereas theta and gamma frequencies were observed in relation to the verbal condition. For the monolingual group, oscillatory activity measures were associated with facilitation trials. All together, these different measures indicate a complex impact of bilingualism on neural networks and highlight differential processing/strategies and allocation of attentional resources to avoid interference for the two groups.

Similarly, with a subset of the participants from Pereira Soares et al. (Reference Pereira Soares, Kubota, Rossi and Rothman2021), Pereira Soares, Prystauka, DeLuca and Rothman (2022) used TFRs to investigate how different types of bilinguals perform on a task of inhibitory control (i.e., Flanker task). The difference between incongruent and congruent trials was computed and used to (i) compare brain correlates between HSs and L2 learners (ii) look into individual differences in language experience of bilingualism at the individual and group (bilingual type: early versus late) level (iii) investigate if neural measures predict behavioral performance. No differences were found at the brain level in the between-group comparison. However, regressions of bilingual experiential factors (within each group) to the neuronal data revealed that specific oscillatory correlates of inhibitory control (mainly theta activation and alpha suppression) were modulated by certain experience variables within a given bilingual type. Finally, opposing signatures were observed in the relationship between oscillatory data and RTs. All together, these findings indicate differential shifting and adaptations of brain network recruitment to cope with the cognitive demands involved in variation in bilingual language experience. However, these brain adaptations seem to follow differential trajectories across early versus late bilinguals, highlighting how bilingual experience matters across the board, including the age at which individuals first become bilingual.

Conclusions and Future directions

The study of the brain's oscillatory activity has only very recently started to be utilized to study bilingualism and its neural underpinnings. Herein, we provided a description of the characteristics of the human oscillatory signal, and an in depth description of the various types of analyses that pertain to the EEG oscillatory signal. With these tools explained, we explored the emerging literature on neural oscillations and bilingualism, highlighting how analyzing the human EEG signal through neural oscillations can continue to advance our knowledge and understanding of the dynamicity inherent to linguistic and cognitive processes underlying bilingualism.

As revealed by the most recent literature in the field, language processing and neurocognitive effects are highly variable, especially in bilingualism for which internal and external variables condition the way speakers acquire and use their languages across the lifespan. Recent research in bilingualism has already underscored how differences in bilingual language use can be harnessed to better understand how individuals engage linguistic and cognitive functions differentially. The overall findings from the emerging research in the field indicate that the oscillatory activity of the brain informs processes of neural network recruitment and brain processing adaptations likely due to individual-level demands on bilingual language processing and control. The reduced cost (relative to other neuroimaging), relative accessibility and flexibility of conducting EEG studies in combination with the increased temporal resolution afforded by the analysis of neural oscillations, and the capability of neural oscillations to better discriminate across underlying neural linguistic and cognitive components offers an opportunity to make significant breakthroughs in the study of the effects of bilingualism on linguistic representation, language processing and effects on cognition and brain function. In particular, not least since EEG is portable, neural oscillation analyses (coupled with ERPs) stand out as a method of promise to gather data from more participants with a wider and more representative sampling (i.e., potential for increasing statistical power, Brysbaert, Reference Brysbaert2021, and diversifying the pools of participants, the contexts of bilingualism and the language pairings considered). In addition, as we look at a growing literature in neural oscillations and bilingualism, it will be possible to link the emerging results to other data in human neurocognition, enabling greater connections between neural and behavioral data across fields in human cognition.

To conclude, we welcome the wider diffusion of this technique in the field as a yet additional tool of validity for data found with complementary methods while simultaneously injecting greater ecological validity in bilingual language research at multiple levels. If the goal of the article was successful, researchers interested in bilingualism and the brain/linguistic processing will have the basis required to find their way through the covered growing literatures and apply the theoretical knowledge acquired to inform bilingualism research moving forward.

Financial support

This article was supported by generous funding to Sergio Miguel Pereira Soares by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska Curie Grant Agreement No. 765556. Jason Rothman and Yanina Prystauka were funded by the AcqVA Auora Center grant. Jason Rothman and Yanina Prystauka received funding from the Tromsø Forskningsstiftelse (Tromsø Research Foundation) Grant No. A43484 and the Heritage-bilingual Linguistic Proficiency in their Native Grammar (HeLPiNG) (2019–2023).

Footnotes

*

First listed author developed the conceptualization of the paper; however, the three first listed authors contributed equally to the paper and, thus, share first authorship

The online version of this article has been updated since original publication. A notice detailing the changes has also been published at https://doi.org/10.1017/S1366728923000032.

1 Although we focus here on time-series data recorded with EEG, it is worth mentioning that, in general, magnetoencephalography (MEG) has also been extensively used to study neural oscillations (via recording the fluctuations in the magnetic field produced by changes in the electrical activity of the brain: Hansen, Kringelbach & Salmelin, Reference Hansen, Kringelbach and Salmelin2010).

References

Abutalebi, J, Della Rosa, PA, Green, DW, Hernandez, M, Scifo, P, Keim, R, Cappa, SF and Costa, A (2012) Bilingualism tunes the anterior cingulate cortex for conflict monitoring. Cerebral Cortex 22, 20762086.CrossRefGoogle ScholarPubMed
Abutalebi, J and Green, DW (2016) Neuroimaging of language control in bilinguals: neural adaptation and reserve. Bilingualism: Language and Cognition 19, 689698.CrossRefGoogle Scholar
Akimoto, Y, Takahashi, H, Gunji, A, Kaneko, Y, Asano, M, Matsuo, J, Ota, M, Kunugi, H, Hanakawa, T, Mazuka, R and Kamio, Y (2017) Alpha band event-related desynchronization underlying social situational context processing during irony comprehension: A magnetoencephalography source localization study. Brain and Language 175, 4246.CrossRefGoogle ScholarPubMed
Alemán-Bañón, J, Fiorentino, R and Gabriele, A (2018) Using event-related potentials to track morphosyntactic development in second language learners: The processing of number and gender agreement in Spanish. PloS One 13, e0200791.CrossRefGoogle ScholarPubMed
Anderson, AJ and Perone, S (2018) Developmental change in the resting state electroencephalogram: Insights into cognition and the brain. Brain and Cognition 126, 4052.CrossRefGoogle ScholarPubMed
Bakker, I, Takashima, A, Van Hell, JG, Janzen, G and McQueen, JM (2015) Changes in theta and beta oscillations as signatures of novel word consolidation. Journal of Cognitive Neuroscience 27, 12861297.CrossRefGoogle ScholarPubMed
Bakker-Marshall, I, Takashima, A, Schoffelen, JM, Van Hell, JG, Janzen, G and McQueen, JM (2018) Theta-band oscillations in the middle temporal gyrus reflect novel word consolidation. Journal of Cognitive Neuroscience 30, 621633.CrossRefGoogle ScholarPubMed
Barac, R, Moreno, S and Bialystok, E (2016) Behavioral and electrophysiological differences in executive control between monolingual and bilingual children. Child Development 87, 12771290.CrossRefGoogle ScholarPubMed
Bastiaansen, M, Van Berkum, JJ and Hagoort, P (2003) Syntactic processing modulates the θ rhythm of the human EEG. NeuroImage 17, 14791492.CrossRefGoogle Scholar
Bastiaansen, M and Hagoort, P (2006) Oscillatory neuronal dynamics during language comprehension. Progress in Brain Research 159, 179196.CrossRefGoogle ScholarPubMed
Bastiaansen, M, Magyari, L and Hagoort, P (2010) Syntactic unification operations are reflected in oscillatory dynamics during on-line sentence comprehension. Journal of Cognitive Neuroscience 22, 1333–134CrossRefGoogle ScholarPubMed
Bastiaansen, M, Mazaheri, A and Jensen, O (2012) Beyond ERPs: Oscillatory neuronal dynamics The Oxford handbook of event-related potential components. New York, NY, US: Oxford University Press, pp. 3149.Google Scholar
Bastos, AM, Usrey, WM, Adams, RA, Mangun, GR, Fries, P and Friston, KJ (2012) Canonical microcircuits for predictive coding. Neuron 76, 695711.CrossRefGoogle ScholarPubMed
Beatty-Martínez, AL and Dussias, PE (2019) Revisiting masculine and feminine grammatical gender in Spanish: Linguistic, psycholinguistic, and neurolinguistic evidence. Frontiers in Psychology 10, 751.CrossRefGoogle ScholarPubMed
Beres, AM (2017) Time is of the essence: A review of electroencephalography (EEG) and event-related brain potentials (ERPs) in language research. Applied Psychophysiology and Biofeedback 42, 247255.CrossRefGoogle Scholar
Bialystok, E (2021) Bilingualism as a Slice of Swiss Cheese. Frontiers in Psychology, 5219.CrossRefGoogle ScholarPubMed
Bialystok, E and Craik, F (2022) How does bilingualism modify cognitive function? Attention to the mechanism. Psychonomic Bulletin & Review, 124.Google ScholarPubMed
Bialystok, E, Craik, FI, Grady, C, Chau, W, Ishii, R, Gunji, A and Pantev, C (2005) Effect of bilingualism on cognitive control in the Simon task: Evidence from MEG. NeuroImage 24, 4049.CrossRefGoogle ScholarPubMed
Bice, K, Yamasaki, BL and Prat, CS (2020) Bilingual language experience shapes resting-state brain rhythms. Neurobiology of Language 1, 288318.CrossRefGoogle ScholarPubMed
Blanco-Elorrieta, E, Ding, N, Pylkkänen, L and Poeppel, D (2019) Understanding requires tracking: noise and knowledge interact in bilingual comprehension. BioRxiv, 609628.Google Scholar
Blumenfeld, HK and Marian, V (2007) Constraints on parallel activation in bilingual spoken language processing: Examining proficiency and lexical status using eye-tracking. Language and Cognitive Processes 22, 633660.CrossRefGoogle Scholar
Blumenfeld, HK and Marian, V (2013) Parallel language activation and cognitive control during spoken word recognition in bilinguals. Journal of Cognitive Psychology 25, 547567.CrossRefGoogle ScholarPubMed
Boersma, M, Smit, DJ, de Bie, HM, Van Baal, GCM, Boomsma, DI, de Geus, EJ, Delemarre-van de Waal, HA and Stam, CJ (2011) Network analysis of resting state EEG in the developing young brain: structure comes with maturation. Human Brain Mapping 32, 413425.CrossRefGoogle ScholarPubMed
Botvinick, MM, Braver, TS, Barch, DM, Carter, CS and Cohen, JD (2001) Conflict monitoring and cognitive control. Psychological Review 108, 624.CrossRefGoogle ScholarPubMed
Brazier, MA (1961) A history of the electrical activity of the brain: The first half-century. Macmillan.Google Scholar
Brokaw, K, Tishler, W, Manceor, S, Hamilton, K, Gaulden, A, Parr, E and Wamsley, EJ (2016) Resting state EEG correlates of memory consolidation. Neurobiology of Learning and Memory 130, 1725.CrossRefGoogle ScholarPubMed
Brysbaert, M (2021) Power considerations in bilingualism research: Time to step up our game. Bilingualism: Language and Cognition 24, 813818.CrossRefGoogle Scholar
Burgaleta, M, Sanjuán, A, Ventura-Campos, N, Sebastian-Galles, N and Ávila, C (2016) Bilingualism at the core of the brain. Structural differences between bilinguals and monolinguals revealed by subcortical shape analysis. NeuroImage 125, 437445.CrossRefGoogle ScholarPubMed
Burns, SP, Santaniello, S, Yaffe, RB, Jouny, CC, Crone, NE, Bergey, GK, Anderson, WS and Sarma, SV (2014) Network dynamics of the brain and influence of the epileptic seizure onset zone. Proceedings of the National Academy of Sciences 111, E5321E5330.CrossRefGoogle ScholarPubMed
Buzsáki, G (2006) Rhythms of the Brain. Oxford University Press.CrossRefGoogle Scholar
Buzsáki, G and Watson, BO (2012) Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues in Clinical Neuroscience 14, 345.CrossRefGoogle ScholarPubMed
Calvo, N and Bialystok, E (2021) Electrophysiological signatures of attentional control in bilingual processing: Evidence from proactive interference. Brain and Language 222, 105027.CrossRefGoogle ScholarPubMed
Canal, P, Pesciarelli, F, Vespignani, F, Molinaro, N and Cacciari, C (2017) Basic composition and enriched integration in idiom processing: An EEG study. Journal of Experimental Psychology: Learning, Memory, and Cognition 43, 928.Google ScholarPubMed
Cassani, R, Estarellas, M, San-Martin, R, Fraga, FJ and Falk, TH (2018) Systematic review on resting-state EEG for Alzheimer's disease diagnosis and progression assessment. Disease Markers, 2018.CrossRefGoogle ScholarPubMed
Cavanagh, JF and Frank, MJ (2014) Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences 18, 414421.CrossRefGoogle ScholarPubMed
Cespón, J and Carreiras, M (2020) Is there electrophysiological evidence for a bilingual advantage in neural processes related to executive functions?. Neuroscience & Biobehavioral Reviews 118, 315330.CrossRefGoogle ScholarPubMed
Chang, C, Liu, Z, Chen, MC, Liu, X and Duyn, JH (2013) EEG correlates of time-varying BOLD functional connectivity. NeuroImage 72, 227236.CrossRefGoogle ScholarPubMed
Clahsen, H and Felser, C (2006) How native-like is non-native language processing?. Trends in Cognitive Sciences 10, 564570.CrossRefGoogle ScholarPubMed
Clahsen, H and Felser, C (2018) Some notes on the shallow structure hypothesis. Studies in Second Language Acquisition 40, 693706.CrossRefGoogle Scholar
Cole, S and Voytek, B (2019) Cycle-by-cycle analysis of neural oscillations. Journal of Neurophysiology 122, 849861.CrossRefGoogle ScholarPubMed
Coughlin, CE and Tremblay, A (2011) Native and non-native processing of short and long agreement dependencies in French. In Proceedings of the 2011 Annual Conference of the Canadian Linguistic Association.Google Scholar
Cross, ZR, Corcoran, AW, Schlesewsky, M, Kohler, MJ and Bornkessel-Schlesewsky, I (2020) Oscillatory and aperiodic neural activity jointly predict grammar learning. BioRxiv.CrossRefGoogle Scholar
Davidson, DJ and Indefrey, P (2007) An inverse relation between event-related and time–frequency violation responses in sentence processing. Brain Research 1158, 8192.CrossRefGoogle ScholarPubMed
De Hemptinne, C, Swann, NC, Ostrem, JL, Ryapolova-Webb, ES, San Luciano, M, Galifianakis, NB and Starr, PA (2015) Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson's disease. Nature Neuroscience 18, 779786.CrossRefGoogle ScholarPubMed
DeLuca, V, Miller, D, Swanson, K and Rothman, J (to appear) Neuro Methods in Formal Linguistic Approaches to Second Language Acquisition/Processing. In Morgan-Short, K and van Hell, J (eds.) The Routledge Handbook of SLA and Neurolinguistics.Google Scholar
DeLuca, V, Miller, D, Pliatsikas, C and Rothman, J (2019a) Brain adaptations and neurological indices of processing in adult Second Language Acquisition: Challenges for the Critical Period Hypothesis. In Schwieter, J, The Handbook of the Neuroscience of Multilingualism. Wiley-Blackwell. Hoboken: NJGoogle Scholar
DeLuca, V, Rothman, J, Bialystok, E and Pliatsikas, C (2019b) Redefining bilingualism as a spectrum of experiences that differentially affects brain structure and function. Proceedings of the National Academy of Sciences 116, 75657574.CrossRefGoogle ScholarPubMed
DeLuca, V, Segaert, K, Mazaheri, A and Krott, A (2020) Understanding bilingual brain function and structure changes? U Bet! A Unified Bilingual Experience Trajectory model. Journal of Neurolinguistics 56, 100930.CrossRefGoogle Scholar
Donner, TH and Siegel, M (2011) A framework for local cortical oscillation patterns. Trends in Cognitive Sciences 15, 191199.CrossRefGoogle ScholarPubMed
Donoghue, T, Haller, M, Peterson, EJ, Varma, P, Sebastian, P, Gao, R, Noto, T, Lara, AH, Wallis, JD, Knight, RT, Shestyuk, A and Voytek, B (2020) Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience 23, 16551665.CrossRefGoogle ScholarPubMed
Donoghue, T, Schaworonkow, N and Voytek, B (2021) Methodological considerations for studying neural oscillations. European Journal of Neuroscience 55, 35023527. https://doi.org/10.1111/ejn.15361CrossRefGoogle ScholarPubMed
Dussias, PE and Sagarra, N (2007) The effect of exposure on syntactic parsing in Spanish–English bilinguals. Bilingualism: Language and Cognition 10, 101116.CrossRefGoogle Scholar
Engel, AK and Fries, P (2010) Beta-band oscillations – signalling the status quo?. Current Opinion in Neurobiology 20, 156165.CrossRefGoogle ScholarPubMed
Fell, J (2007) Cognitive neurophysiology: beyond averaging. NeuroImage 37, 10691072.CrossRefGoogle ScholarPubMed
Fernandez, FR, Noueihed, J and White, JA (2019) Voltage-dependent membrane properties shape the size but not the frequency content of spontaneous voltage fluctuations in layer 2/3 somatosensory cortex. Journal of Neuroscience 39, 22212237.CrossRefGoogle Scholar
Fernández, M, Tartar, JL, Padron, D and Acosta, J (2013) Neurophysiological marker of inhibition distinguishes language groups on a non-linguistic executive function test. Brain and Cognition 83, 330336.CrossRefGoogle ScholarPubMed
Foucart, A and Frenck-Mestre, C (2012) Can late L2 learners acquire new grammatical features? Evidence from ERPs and eye-tracking. Journal of Memory and Language 66, 226248.CrossRefGoogle Scholar
Freeman, WJ, Burke, BC and Holmes, MD (2003) Aperiodic phase re-setting in scalp EEG of beta–gamma oscillations by state transitions at alpha–theta rates. Human Brain Mapping 19, 248272.CrossRefGoogle ScholarPubMed
Gabriele, A, Fiorentino, R and Alemán-Bañón, J (2013) Examining second language development using event-related potentials: A cross-sectional study on the processing of gender and number agreement. Linguistic Approaches to Bilingualism 3, 213232.CrossRefGoogle Scholar
Gabrys, RL, Tabri, N, Anisman, H and Matheson, K (2018) Cognitive control and flexibility in the context of stress and depressive symptoms: The cognitive control and flexibility questionnaire. Frontiers in Psychology 9, 2219.CrossRefGoogle ScholarPubMed
Giraud, AL and Poeppel, D (2012) Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience 15, 511.CrossRefGoogle ScholarPubMed
Grabner, RH, Brunner, C, Leeb, R, Neuper, C and Pfurtscheller, G (2007) Event-related EEG theta and alpha band oscillatory responses during language translation. Brain Research Bulletin 72, 5765.CrossRefGoogle ScholarPubMed
Grady, CL, Luk, G, Craik, FI and Bialystok, E (2015) Brain network activity in monolingual and bilingual older adults. Neuropsychologia 66, 170181.CrossRefGoogle ScholarPubMed
Gray, CM, König, P, Engel, AK and Singer, W (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334337.CrossRefGoogle ScholarPubMed
Green, DW (1998) Mental control of the bilingual lexico-semantic system. Bilingualism: Language and Cognition 1, 6781.CrossRefGoogle Scholar
Gröchenig, K (2001) Foundations of time-frequency analysis. Springer Science & Business Media.CrossRefGoogle Scholar
Groom, MJ and Cragg, L (2015) Differential modulation of the N2 and P3 event-related potentials by response conflict and inhibition. Brain and Cognition 97, 19.CrossRefGoogle ScholarPubMed
Gross, J (2014) Analytical methods and experimental approaches for electrophysiological studies of brain oscillations. Journal of Neuroscience Methods 228, 5766.CrossRefGoogle ScholarPubMed
Grundy, JG (2020) The effects of bilingualism on executive functions: An updated quantitative analysis. Journal of Cultural Cognitive Science 4, 177199.CrossRefGoogle Scholar
Grundy, JG, Anderson, JA and Bialystok, E (2017) Neural correlates of cognitive processing in monolinguals and bilinguals. Annals of the New York Academy of Sciences 1396, 183201.CrossRefGoogle ScholarPubMed
Gullifer, JW, Chai, XJ, Whitford, V, Pivneva, I, Baum, S, Klein, D and Titone, D (2018) Bilingual experience and resting-state brain connectivity: Impacts of L2 age of acquisition and social diversity of language use on control networks. Neuropsychologia 117, 123134.CrossRefGoogle ScholarPubMed
Gullifer, JW and Titone, D (2020) Characterizing the social diversity of bilingualism using language entropy. Bilingualism: Language and Cognition 23, 283294.CrossRefGoogle Scholar
Hagoort, P (2005) On Broca, brain, and binding: a new framework. Trends in Cognitive Sciences 9, 416423.CrossRefGoogle Scholar
Handy, TC (Ed.). (2005) Event-related potentials: A methods handbook. MIT press.Google Scholar
Hansen, P, Kringelbach, M and Salmelin, R (Eds.). (2010) MEG: an introduction to methods. Oxford university press.CrossRefGoogle Scholar
He, BJ (2014) Scale-free brain activity: past, present, and future. Trends in Cognitive Sciences 18, 480487.CrossRefGoogle ScholarPubMed
Hebb, DO (1949) The first stage of perception: growth of the assembly. The Organization of Behavior 4, 6078.Google Scholar
Heil, M, Osman, A, Wiegelmann, J, Rolke, B and Hennighausen, E (2000) N200 in the Eriksen-task: Inhibitory executive process?. Journal of Psychophysiology 14, 218.CrossRefGoogle Scholar
Herrmann, CS, Munk, MH and Engel, AK (2004) Cognitive functions of gamma-band activity: memory match and utilization. Trends in Cognitive Sciences 8, 347355.CrossRefGoogle ScholarPubMed
Heunis, T, Aldrich, C, Peters, JM, Jeste, SS, Sahin, M, Scheffer, C and De Vries, PJ (2018) Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder–a systematic methodological exploration of technical and demographic confounders in the search for biomarkers. BMC Medicine 16, 117.CrossRefGoogle ScholarPubMed
Holmes, GL and Khazipov, R (2007) Basic neurophysiology and the cortical basis of EEG. In Blum, AS and Rutkove, SB (Eds.), The Clinical Neurophysiology Primer, 1933, Humana Press.CrossRefGoogle Scholar
Hoshino, N and Thierry, G (2011) Language selection in bilingual word production: Electrophysiological evidence for cross-language competition. Brain Research 1371, 100109.CrossRefGoogle ScholarPubMed
Hsieh, LT and Ranganath, C (2014) Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. NeuroImage 85, 721729.CrossRefGoogle ScholarPubMed
Hurtado, JM, Rubchinsky, LL and Sigvardt, KA (2004) Statistical method for detection of phase-locking episodes in neural oscillations. Journal of Neurophysiology 91, 18831898.CrossRefGoogle ScholarPubMed
Immink, MA, Cross, ZR, Chatburn, A, Baumeister, J, Schlesewsky, M and Bornkessel-Schlesewsky, I (2021) Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning. Human Movement Science 78, 102829.CrossRefGoogle ScholarPubMed
Jabès, A, Klencklen, G, Ruggeri, P, Michel, CM, Lavenex, PB and Lavenex, P (2021) Resting-State EEG Microstates Parallel Age-Related Differences in Allocentric Spatial Working Memory Performance. Brain Topography 34, 442460.CrossRefGoogle ScholarPubMed
Jensen, O and Mazaheri, A (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Frontiers in Human Neuroscience 4, 186.CrossRefGoogle ScholarPubMed
Kajikawa, Y and Schroeder, CE (2011) How local is the local field potential?. Neuron 72, 847858.CrossRefGoogle ScholarPubMed
Kam, JW, Bolbecker, AR, O'Donnell, BF, Hetrick, WP and Brenner, CA (2013) Resting state EEG power and coherence abnormalities in bipolar disorder and schizophrenia. Journal of Psychiatric Research 47, 18931901.CrossRefGoogle ScholarPubMed
Kielar, A, Meltzer, JA, Moreno, S, Alain, C and Bialystok, E (2014) Oscillatory responses to semantic and syntactic violations. Journal of Cognitive Neuroscience 26, 28402862.CrossRefGoogle ScholarPubMed
Kielar, A, Panamsky, L, Links, KA and Meltzer, JA (2015) Localization of electrophysiological responses to semantic and syntactic anomalies in language comprehension with MEG. NeuroImage 105, 507524.CrossRefGoogle ScholarPubMed
Kirschstein, T and Köhling, R (2009) What is the source of the EEG?. Clinical EEG and Neuroscience 40, 146149.CrossRefGoogle ScholarPubMed
Kliesch, M, Giroud, N and Meyer, M (2020) EEG Resting-State and Event-Related Potentials as Markers of Learning Success in Older Adults Following Second Language Training: A Pilot Study. Brain Plasticity 7, 143162.CrossRefGoogle Scholar
Klimesch, W, Sauseng, P and Hanslmayr, S (2007) EEG alpha oscillations: the inhibition–timing hypothesis. Brain Research Reviews 53, 6388.CrossRefGoogle ScholarPubMed
König, P and Schillen, TB (1991) Stimulus-dependent assembly formation of oscillatory responses: I. Synchronization. Neural Computation 3, 155166.CrossRefGoogle ScholarPubMed
Kösem, A and van Wassenhove, V (2017) Distinct contributions of low-and high-frequency neural oscillations to speech comprehension. Language, Cognition and Neuroscience 32, 536544.CrossRefGoogle Scholar
Kousaie, S and Phillips, NA (2012) Ageing and bilingualism: Absence of a “bilingual advantage” in Stroop interference in a nonimmigrant sample. Quarterly Journal of Experimental Psychology 65, 356369.CrossRefGoogle Scholar
Kousaie, S and Phillips, NA (2017) A behavioural and electrophysiological investigation of the effect of bilingualism on aging and cognitive control. Neuropsychologia 94, 2335.CrossRefGoogle ScholarPubMed
Kroll, JF, Bobb, SC and Wodniecka, Z (2006) Language selectivity is the exception, not the rule: Arguments against a fixed locus of language selection in bilingual speech. Bilingualism: Language and Cognition 9, 119135.CrossRefGoogle Scholar
Kroll, JF, Bobb, SC, Misra, M and Guo, T (2008) Language selection in bilingual speech: Evidence for inhibitory processes. Acta Psychologica 128, 416430.CrossRefGoogle ScholarPubMed
Kroll, JF, Dussias, PE, Bogulski, CA and Valdés-Kroff, JR (2012) Juggling two languages in one mind: What bilinguals tell us about language processing and its consequences for cognition. In Ross, BH (Ed.), Psychology of Learning and Motivation, Vol. 56, Academic Press pp. 229262.Google Scholar
Kroll, JF and Bialystok, E (2013) Understanding the consequences of bilingualism for language processing and cognition. Journal of Cognitive Psychology 25, 497514.CrossRefGoogle ScholarPubMed
Kroll, JF, Gullifer, JW, McClain, R, Rossi, E and Martín, MC (2015) Selection and control in bilingual comprehension and production. In Schweiter, J, (Ed.), Cambridge Handbook of Bilingualism. New York: Cambridge University Press.Google Scholar
Kuhl, PK, Stevenson, J, Corrigan, NM, van den Bosch, JJ, Can, DD and Richards, T (2016) Neuroimaging of the bilingual brain: Structural brain correlates of listening and speaking in a second language. Brain and Language 162, 19.CrossRefGoogle Scholar
Kupisch, T and Rothman, J (2018) Terminology matters! Why difference is not incompleteness and how early child bilinguals are heritage speakers. International Journal of Bilingualism 22, 564582.CrossRefGoogle Scholar
Lam, KJ, Bastiaansen, MC, Dijkstra, T and Rueschemeyer, SA (2017) Making sense: Motor activation and action plausibility during sentence processing. Language, Cognition and Neuroscience 32, 590600.CrossRefGoogle Scholar
Land, R, Engler, G, Kral, A and Engel, AK (2013) Response properties of local field potentials and multiunit activity in the mouse visual cortex. Neuroscience 254, 141151.CrossRefGoogle ScholarPubMed
Larson, C, Kaplan, D, Kaushanskaya, M and Weismer, SE (2020) Language and inhibition: Predictive relationships in children with language impairment relative to typically developing peers. Journal of Speech, Language, and Hearing Research 63, 11151127.CrossRefGoogle ScholarPubMed
Leivada, E, Mitrofanova, N and Westergaard, M (2021b) Bilinguals are better than monolinguals in detecting manipulative discourse. PloS One 16, e0256173.CrossRefGoogle ScholarPubMed
Leivada, E, Westergaard, M, Duñabeitia, JA and Rothman, J (2021a) On the phantom-like appearance of bilingualism effects on neurocognition:(How) should we proceed?. Bilingualism: Language and Cognition 24, 197210.CrossRefGoogle Scholar
Levy, BJ, McVeigh, ND, Marful, A and Anderson, MC (2007) Inhibiting your native language: The role of retrieval-induced forgetting during second-language acquisition. Psychological Science 18, 2934.CrossRefGoogle ScholarPubMed
Lewis, AG, Schoffelen, JM, Schriefers, H and Bastiaansen, M (2016) A predictive coding perspective on beta oscillations during sentence-level language comprehension. Frontiers in Human Neuroscience 10, 85.CrossRefGoogle ScholarPubMed
Lewis, AG, Schoffelen, JM, Hoffmann, C, Bastiaansen, M and Schriefers, H (2017) Discourse-level semantic coherence influences beta oscillatory dynamics and the N400 during sentence comprehension. Language, Cognition and Neuroscience 32, 601617.CrossRefGoogle Scholar
Li, L, Abutalebi, J, Zou, L, Yan, X, Liu, L, Feng, X, Wang, R, Guo, T and Ding, G (2015) Bilingualism alters brain functional connectivity between “control” regions and “language” regions: Evidence from bimodal bilinguals. Neuropsychologia 71, 236247.CrossRefGoogle ScholarPubMed
Linck, JA, Kroll, JF and Sunderman, G (2009) Losing access to the native language while immersed in a second language: Evidence for the role of inhibition in second-language learning. Psychological Science 20, 15071515.CrossRefGoogle Scholar
Lisman, J (2010) Working memory: the importance of theta and gamma oscillations. Current Biology 20, R490R492.CrossRefGoogle ScholarPubMed
Lisman, JE and Jensen, O (2013) The theta-gamma neural code. Neuron 77, 10021016.CrossRefGoogle Scholar
Luck, SJ (2012) Electrophysiological correlates of the focusing of attention within complex visual scenes: N2pc and related ERP components. In Luck, SJ and Kappenman, ES (Eds.), The Oxford handbook of event-related potential components. Oxford University Press, pp. 329360.Google Scholar
Luck, SJ and Kappenman, ES (Eds.). (2011) The Oxford handbook of event-related potential components. Oxford university press.Google Scholar
Luk, G, Anderson, JA, Craik, FI, Grady, C and Bialystok, E (2010) Distinct neural correlates for two types of inhibition in bilinguals: Response inhibition versus interference suppression. Brain and Cognition 74, 347357.CrossRefGoogle ScholarPubMed
Luk, G, Green, DW, Abutalebi, J and Grady, C (2012) Cognitive control for language switching in bilinguals: A quantitative meta-analysis of functional neuroimaging studies. Language and Cognitive Processes 27, 14791488.CrossRefGoogle Scholar
Luk, G and Bialystok, E (2013) Bilingualism is not a categorical variable: Interaction between language proficiency and usage. Journal of Cognitive Psychology 25, 605621.CrossRefGoogle Scholar
Luk, G, Pliatsikas, C and Rossi, E (2020) Brain changes associated with language development and learning: A primer on methodology and applications. System 89, 102209.CrossRefGoogle Scholar
Luo, H and Poeppel, D (2007) Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. Neuron 54, 10011010.CrossRefGoogle ScholarPubMed
Marinazzo, D, Riera, JJ, Marzetti, L, Astolfi, L, Yao, D and Valdés Sosa, PA (2019) Controversies in EEG source imaging and connectivity: modeling, validation, benchmarking. Brain Topography 32, 527529.CrossRefGoogle ScholarPubMed
Maris, E and Oostenveld, R (2007) Nonparametric statistical testing of EEG-and MEG-data. Journal of Neuroscience Methods 164, 177190.CrossRefGoogle ScholarPubMed
Martin, CD, Thierry, G, Kuipers, JR, Boutonnet, B, Foucart, A and Costa, A (2013) Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language 69, 574588.CrossRefGoogle Scholar
Mazaheri, A, Segaert, K, Olichney, J, Yang, JC, Niu, YQ, Shapiro, K and Bowman, H (2018) EEG oscillations during word processing predict MCI conversion to Alzheimer's disease. NeuroImage: Clinical 17, 188197.CrossRefGoogle ScholarPubMed
McCarley, RW, Nakamura, M, Shenton, ME and Salisbury, DF (2008) Combining ERP and structural MRI information in first episode schizophrenia and bipolar disorder. Clinical EEG and Neuroscience 39, 5760.CrossRefGoogle ScholarPubMed
McCauley, SM, Isbilen, ES and Christiansen, MH (2017) Chunking Ability Shapes Sentence Processing at Multiple Levels of Abstraction. Cognitive Science.Google Scholar
McLaughlin, J, Osterhout, L and Kim, A (2004) Neural correlates of second-language word learning: Minimal instruction produces rapid change. Nature Neuroscience 7, 703704.CrossRefGoogle ScholarPubMed
Mechelli, A, Crinion, JT, Noppeney, U, O'Doherty, J, Ashburner, J, Frackowiak, RS and Price, CJ (2004) Structural plasticity in the bilingual brain. Nature 431, 757757.CrossRefGoogle ScholarPubMed
Meghdadi, AH, Stevanović Karić, M, McConnell, M, Rupp, G, Richard, C, Hamilton, J, Salat, D and Berka, C (2021) Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PloS One 16, e0244180.CrossRefGoogle ScholarPubMed
Meltzer, JA and Braun, AR (2011) An EEG–MEG dissociation between online syntactic comprehension and post hoc reanalysis. Frontiers in Human Neuroscience 5, 10.CrossRefGoogle ScholarPubMed
Meyer, L (2018) The neural oscillations of speech processing and language comprehension: state of the art and emerging mechanisms. European Journal of Neuroscience 48, 26092621.CrossRefGoogle ScholarPubMed
Meyer, L, Grigutsch, M, Schmuck, N, Gaston, P and Friederici, AD (2015) Frontal–posterior theta oscillations reflect memory retrieval during sentence comprehension. Cortex 71, 205218.CrossRefGoogle ScholarPubMed
Meyer, L, Sun, Y and Martin, AE (2020) Synchronous, but not entrained: exogenous and endogenous cortical rhythms of speech and language processing. Language, Cognition and Neuroscience 35, 10891099.CrossRefGoogle Scholar
Meyer, L, Sun, Y and Martin, AE (2020b) “Entraining” to speech, generating language?. Language, Cognition and Neuroscience 35, 11381148.CrossRefGoogle Scholar
Mohades, SG, Van Schuerbeek, P, Rosseel, Y., Van De Craen, P, Luypaert, R and Baeken, C (2015) White-matter development is different in bilingual and monolingual children: a longitudinal DTI study. PloS One 10, e0117968.CrossRefGoogle ScholarPubMed
Morales, J, Yudes, C, Gómez-Ariza, CJ and Bajo, MT (2015) Bilingualism modulates dual mechanisms of cognitive control: Evidence from ERPs. Neuropsychologia 66, 157169.CrossRefGoogle ScholarPubMed
Moreno, I, de Vega, M and León, I (2013) Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions. Brain and Cognition 82, 236242.CrossRefGoogle Scholar
Moreno, I, de Vega, M, León, I, Bastiaansen, M, Lewis, AG and Magyari, L (2015) Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms. NeuroImage 109, 5062.CrossRefGoogle Scholar
Moreno, S, Bialystok, E, Wodniecka, Z and Alain, C (2010) Conflict resolution in sentence processing by bilinguals. Journal of Neurolinguistics 23, 564579.CrossRefGoogle ScholarPubMed
Morgan-Short, K, Steinhauer, K, Sanz, C and Ullman, MT (2012) Explicit and implicit second language training differentially affect the achievement of native-like brain activation patterns. Journal of Cognitive Neuroscience 24, 933947.CrossRefGoogle ScholarPubMed
Morrison, C, Kamal, F and Taler, V (2019) The influence of bilingualism on working memory event-related potentials. Bilingualism: Language and Cognition 22, 191199.CrossRefGoogle Scholar
Murias, M, Webb, SJ, Greenson, J and Dawson, G (2007) Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biological Psychiatry 62, 270273.CrossRefGoogle ScholarPubMed
Newson, JJ and Thiagarajan, TC (2019) EEG frequency bands in psychiatric disorders: a review of resting state studies. Frontiers in Human Neuroscience 12, 521.CrossRefGoogle ScholarPubMed
Nieuwland, MS and Martin, AE (2017) Neural oscillations and a nascent corticohippocampal theory of reference. Journal of Cognitive Neuroscience 29, 896910.CrossRefGoogle Scholar
Nozari, N and Pinet, S (2020) A critical review of the behavioral, neuroimaging, and electrophysiological studies of co-activation of representations during word production. Journal of Neurolinguistics 53, 100875.CrossRefGoogle Scholar
Nuwer, MR (2010) Electrocorticography and intraoperative electroencephalography. Intraoperative Neurophysiologic Monitoring 77.CrossRefGoogle Scholar
Olejniczak, P (2006) Neurophysiologic basis of EEG. Journal of Clinical Neurophysiology 23, 186189.CrossRefGoogle ScholarPubMed
Olulade, OA, Jamal, NI, Koo, DS, Perfetti, CA, LaSasso, C and Eden, GF (2015) Neuroanatomical evidence in support of the bilingual advantage theory. Cerebral Cortex 26, 31963204.CrossRefGoogle ScholarPubMed
Oostenveld, R, Fries, P, Maris, E and Schoffelen, JM (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011.CrossRefGoogle ScholarPubMed
Osterhout, L, Poliakov, A, Inoue, K, McLaughlin, J, Valentine, G, Pitkanen, I, Frenck-Mestre, C and Hirschensohn, J (2008) Second-language learning and changes in the brain. Journal of Neurolinguistics 21, 509521.CrossRefGoogle ScholarPubMed
Papandreou-Suppappola, A (Ed.). (2018) Applications in time-frequency signal processing. CRC press.CrossRefGoogle Scholar
Pereira Soares, SM, Ong, G, Abutalebi, J, Del Maschio, N, Sewell, D and Weekes, B (2019) A diffusion model approach to analyzing performance on the Flanker task: The role of the DLPFC. Bilingualism: Language and Cognition 22, 11941208.CrossRefGoogle Scholar
Pereira Soares, SM, Kubota, M, Rossi, E and Rothman, J (2021) Determinants of bilingualism predict dynamic changes in resting state EEG oscillations. Brain and Language 223, 105030.CrossRefGoogle ScholarPubMed
Pereira Soares, SM, Prystauka, Y, DeLuca, V and Rothman, J (2022) Type of bilingualism conditions individual differences in the oscillatory dynamics of inhibitory control. Frontiers in Human Neuroscience. 16:910910. doi: 10.3389/fnhum.2022.910910.CrossRefGoogle ScholarPubMed
Pfurtscheller, G and Berghold, A (1989) Patterns of cortical activation during planning of voluntary movement. Electroencephalography and Clinical Neurophysiology 72, 250258.CrossRefGoogle ScholarPubMed
Philipp, AM, Gade, M and Koch, I (2007) Inhibitory processes in language switching: Evidence from switching language-defined response sets. European Journal of Cognitive Psychology 19, 395416.CrossRefGoogle Scholar
Piai, V, Anderson, KL, Lin, JJ, Dewar, C, Parvizi, J, Dronkers, NF and Knight, RT (2016) Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proceedings of the National Academy of Sciences 113, 1136611371.CrossRefGoogle ScholarPubMed
Pliatsikas, C (2019) Multilingualism and brain plasticity. In Schwieter, JW and Paradis, M (Eds.), The handbook of the neuroscience of multilingualism, 230251, Wiley Blackwell.CrossRefGoogle Scholar
Pliatsikas, C (2020) Understanding structural plasticity in the bilingual brain: The Dynamic Restructuring Model. Bilingualism: Language and Cognition 23, 459471.CrossRefGoogle Scholar
Pliatsikas, C, Johnstone, T and Marinis, T (2014) Grey matter volume in the cerebellum is related to the processing of grammatical rules in a second language: a structural voxel-based morphometry study. The Cerebellum 13, 5563.CrossRefGoogle Scholar
Pliatsikas, C, DeLuca, V, Moschopoulou, E and Saddy, JD (2017) Immersive bilingualism reshapes the core of the brain. Brain Structure and Function 222, 17851795.CrossRefGoogle ScholarPubMed
Pliatsikas, C, Pereira Soares, SM, Voits, T, DeLuca, V and Rothman, J (2021) Bilingualism is a long-term cognitively challenging experience that modulates metabolite concentrations in the healthy brain. Scientific Reports 11, 112.CrossRefGoogle ScholarPubMed
Podvalny, E, Noy, N, Harel, M, Bickel, S, Chechik, G, Schroeder, CE, Mehta, AD, Tsodyks, M and Malach, R (2015) A unifying principle underlying the extracellular field potential spectral responses in the human cortex. Journal of Neurophysiology 114, 505519.CrossRefGoogle ScholarPubMed
Prat, CS, Yamasaki, BL, Kluender, RA and Stocco, A (2016) Resting-state qEEG predicts rate of second language learning in adults. Brain and Language 157, 4450.CrossRefGoogle ScholarPubMed
Prat, CS, Yamasaki, BL and Peterson, ER (2019) Individual differences in resting-state brain rhythms uniquely predict second language learning rate and willingness to communicate in adults. Journal of Cognitive Neuroscience 31, 7894.CrossRefGoogle ScholarPubMed
Prat, CS, Madhyastha, TM, Mottarella, MJ and Kuo, CH (2020) Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages. Scientific Reports 10, 110.CrossRefGoogle ScholarPubMed
Prystauka, Y and Lewis, AG (2019) The power of neural oscillations to inform sentence comprehension: A linguistic perspective. Language and Linguistics Compass 13, e12347.CrossRefGoogle ScholarPubMed
Raichle, ME and Snyder, AZ (2007) A default mode of brain function: a brief history of an evolving idea. NeuroImage 37, 10831090.CrossRefGoogle ScholarPubMed
Rodríguez-Pujadas, A, Sanjuán, A, Ventura-Campos, N, Román, P, Martin, C, Barceló, F, Costa, A and Ávila, C (2013) Bilinguals use language-control brain areas more than monolinguals to perform non-linguistic switching tasks. PLoS One 8, e73028.CrossRefGoogle ScholarPubMed
Roehm, D, Schlesewsky, M, Bornkessel, I, Frisch, S and Haider, H (2004) Fractionating language comprehension via frequency characteristics of the human EEG. Neuroreport 15, 409412.CrossRefGoogle ScholarPubMed
Rommers, J, Dickson, DS, Norton, JJ, Wlotko, EW and Federmeier, KD (2017) Alpha and theta band dynamics related to sentential constraint and word expectancy. Language, Cognition and Neuroscience 32, 576589.CrossRefGoogle ScholarPubMed
Rommers, J and Federmeier, KD (2018) Lingering expectations: A pseudo-repetition effect for words previously expected but not presented. NeuroImage 183, 263272.CrossRefGoogle Scholar
Rossi, E, Cheng, H, Kroll, JF, Diaz, MT and Newman, SD (2017) Changes in white-matter connectivity in late second language learners: Evidence from diffusion tensor imaging. Frontiers in Psychology 8, 2040.CrossRefGoogle ScholarPubMed
Rossi, E, Newman, S, Kroll, JF and Diaz, MT (2018) Neural signatures of inhibitory control in bilingual spoken production. Cortex 108, 5066.CrossRefGoogle ScholarPubMed
Rossi, E and Prystauka, Y (2019) Oscillatory brain dynamics of pronoun processing in native Spanish speakers and in late second language learners of Spanish. Bilingualism: Language and Cognition 23, 964977.CrossRefGoogle Scholar
Rothman, J, Alonso, JG and Puig-Mayenco, E (2019) Third language acquisition and linguistic transfer. Cambridge University Press.CrossRefGoogle Scholar
Schulz, P and Grimm, A (2019) The age factor revisited: Timing in acquisition interacts with age of onset in bilingual acquisition. Frontiers in Psychology 9, 2732.CrossRefGoogle ScholarPubMed
Sejdić, E, Djurović, I and Jiang, J (2009) Time–frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing 19, 153183.CrossRefGoogle Scholar
Shackman, AJ, McMenamin, BW, Maxwell, JS, Greischar, LL and Davidson, RJ (2010) Identifying robust and sensitive frequency bands for interrogating neural oscillations. Neuroimage 51, 13191333.CrossRefGoogle ScholarPubMed
Siegel, M, Donner, TH and Engel, AK (2012) Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience 13, 121134.CrossRefGoogle ScholarPubMed
Singer, W (1999) Neuronal synchrony: a versatile code for the definition of relations?. Neuron 24, 4965.CrossRefGoogle ScholarPubMed
Sponheim, SR, Clementz, BA, Iacono, WG and Beiser, M (2000) Clinical and biological concomitants of resting state EEG power abnormalities in schizophrenia. Biological Psychiatry 48, 10881097.CrossRefGoogle ScholarPubMed
Stam, CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clinical Neurophysiology 116, 22662301.CrossRefGoogle ScholarPubMed
Steele, VR, Bernat, EM, Van Den Broek, P, Collins, PF, Patrick, CJ and Marsolek, CJ (2013) Separable processes before, during, and after the N400 elicited by previously inferred and new information: Evidence from time–frequency decompositions. Brain Research 1492, 92107.CrossRefGoogle ScholarPubMed
Stein, M, Federspiel, A, Koenig, T, Wirth, M, Strik, W, Wiest, R, Brandeis, D and Dierks, T (2012) Structural plasticity in the language system related to increased second language proficiency. Cortex 48, 458465.CrossRefGoogle ScholarPubMed
Steinhauer, K, Connolly, JF, Stemmer, B and Whitaker, HA (2008) Event-related potentials in the study of language. In Whitaker, HA (Ed.), Concise Encyclopedia of Brain and Language, 91104. Elsevier.Google Scholar
Sulpizio, S, Del Maschio, N, Fedeli, D and Abutalebi, J (2020) Bilingual language processing: A meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews 108, 834853.CrossRefGoogle ScholarPubMed
Tokowicz, N and MacWhinney, B (2005) Implicit and explicit measures of sensitivity to violations in second language grammar: An event-related potential investigation. Studies in Second Language Acquisition 27, 173204.CrossRefGoogle Scholar
Tolentino, LC and Tokowicz, N (2011) Across languages, space, and time: A review of the role of cross-language similarity in L2 (morpho) syntactic processing as revealed by fMRI and ERP methods. Studies in Second Language Acquisition 33, 91125.CrossRefGoogle Scholar
Tsimpli, IM, Peristeri, E and Andreou, M (2016) Narrative production in monolingual and bilingual children with specific language impairment. Applied Psycholinguistics 37, 195216.CrossRefGoogle Scholar
Varela, F, Lachaux, JP, Rodriguez, E and Martinerie, J (2001) The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience 2, 229239.CrossRefGoogle ScholarPubMed
Vassileiou, B, Meyer, L, Beese, C and Friederici, AD (2018) Alignment of alpha-band desynchronization with syntactic structure predicts successful sentence comprehension. NeuroImage 175, 286296.CrossRefGoogle ScholarPubMed
Verhoeven, L, van Leeuwe, J and Vermeer, A (2011) Vocabulary growth and reading development across the elementary school years. Scientific Studies of Reading 15, 825.CrossRefGoogle Scholar
Von Stein, A and Sarnthein, J (2000) Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization. International Journal of Psychophysiology 38, 301313.CrossRefGoogle Scholar
Vos, SH, Gunter, TC, Kolk, HH and Mulder, G (2001) Working memory constraints on syntactic processing: An electrophysiological investigation. Psychophysiology 38, 4163.CrossRefGoogle ScholarPubMed
Voytek, B, Kramer, MA, Case, J, Lepage, KQ, Tempesta, ZR, Knight, RT and Gazzaley, A (2015) Age-related changes in 1/f neural electrophysiological noise. Journal of Neuroscience 35, 1325713265.CrossRefGoogle ScholarPubMed
Wacker, M and Witte, H. J. M. O. I. I. M. (2013) Time-frequency techniques in biomedical signal analysis. Methods of Information in Medicine 52, 279296.Google ScholarPubMed
Wang, L, Jensen, O, Van den Brink, D, Weder, N, Schoffelen, JM, Magyari, L, Hagorrt, P and Bastiaansen, M (2012) Beta oscillations relate to the N400m during language comprehension. Human Brain Mapping 33, 28982912.CrossRefGoogle Scholar
Wang, J, Li, X, Li, D, Li, XL, Han, JS and Wan, Y (2013) Modulation of brain electroencephalography oscillations by electroacupuncture in a rat model of postincisional pain. Evidence-Based Complementary and Alternative Medicine 2013, 160357.Google Scholar
Ward, LM (2003) Synchronous neural oscillations and cognitive processes. Trends in Cognitive Sciences 7, 553559.CrossRefGoogle ScholarPubMed
Weekes, BS, Abutalebi, J, Mak, HKF, Borsa, V, Pereira Soares, SM, Chiu, PW and Zhang, L (2018) Effect of monolingualism and bilingualism in the anterior cingulate cortex: a proton magnetic resonance spectroscopy study in two centers. Letras de Hoje 53, 512.CrossRefGoogle Scholar
Wilson, CJ and Kawaguchi, Y (1996) The origins of two-state spontaneous membrane potential fluctuations of neostriatal spiny neurons. Journal of Neuroscience 16, 23972410.CrossRefGoogle ScholarPubMed
Zahedi, A, Abdel Rahman, R, Stürmer, B and Sommer, W (2019) Common and specific loci of Stroop effects in vocal and manual tasks, revealed by event-related brain potentials and posthypnotic suggestions. Journal of Experimental Psychology: General 148, 1575.CrossRefGoogle ScholarPubMed
Zhang, H, Wu, YJ and Thierry, G (2020) Bilingualism and aging: A focused neuroscientific review. Journal of Neurolinguistics 54, 100890.CrossRefGoogle Scholar
Zou, L, Ding, G, Abutalebi, J, Shu, H and Peng, D (2012) Structural plasticity of the left caudate in bimodal bilinguals. Cortex 48, 11971206.CrossRefGoogle ScholarPubMed
Zunini, RAL, Morrison, C, Kousaie, S and Taler, V (2019) Task switching and bilingualism in young and older adults: A behavioral and electrophysiological investigation. Neuropsychologia 133, 107186.CrossRefGoogle Scholar
Figure 0

Figure 1. Spectrogram of (log) power over frequency depicting the five (to six) classical frequency bands appearing in different colors.

Figure 1

Figure 2. (A) Graphical example of mean coherence or functional connectivity (pink line – see correspondent coherence Matrix highlighted values in panel B) in the gamma band between electrode scalp regions. Coherence was computed by grouping electrodes into five regions of interest (MF = Medial Frontal, LFT = Left Fronto-Temporal, RFT = Right Fronto-Temporal, LP = Left Parietal, RP = Right Parietal). Adapted from Pereira Soares et al. (2021). (B) Hypothetical mean phase coherence matrix of Figure 2A. Mean phase coherence represents statistic interdependencies (usually) between electrodes of pre-specified regions of interest (ROIs) and the degree of functional connectivity between them (Anderson & Perone, 2018; van Diessen et al., 2015). Mean coherence is expressed as a matrix of values in the 0 to 1 range (the closer the value is to 1, the stronger is the connectivity). Mean coherence can also be calculated between adjacent electrodes (i.e., in the same ROI). This, however, was not the case in this example (see grey cells).

Figure 2

Figure 3. Time-frequency representation of a single electrode (upper panel) and its topographical distribution averaged for the time-frequency windows of interest (lower panel). Color in the plots indicates relative power change.

Figure 3

Figure 4. Demonstration of phase-locked (evoked) and non-phase-locked (induced) activity in the simulated EEG time series (on the left) and in the time-frequency domain (on the right), both in individual trials (the upper three panels) and in the average response (the bottom panel). The plots in the bottom panel demonstrate how the phase-locked response is preserved in both the ERP and the average TFR responses, and how the non-phase-locked response is cancelled out in the ERP but is preserved in the average TFR. Adapted from Bastiaansen et al. (2011), implemented based on the code from Cohen, 2014.