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.