Mapping Epileptic Networks with Scalp and Invasive EEG

doi:10.1017/9781108582285.008

Chapter 8 - Mapping Epileptic Networks with Scalp and Invasive EEG

Applications to Epileptogenic Zone Localization and Seizure Prediction

Published online by Cambridge University Press: 06 January 2023

Manel Vila-Vidal and

Adrià Tauste Campo

Edited by

Rod C. Scott and

J. Matthew Mahoney

Show author details

Rod C. Scott: Affiliation:
University of Vermont
J. Matthew Mahoney: Affiliation:
University of Vermont

Book contents

Get access

Summary

Since the early 2000s, the growing field of computational neuroscience has shown remarkable applicability in the study of epilepsy. A number of different and complementary approaches have been applied to brain signals obtained with scalp and invasive electroencephalography (EEG) to address a variety of fundamental and clinical problems. Historically, researchers have focused on overt changes in brain electrical signals, which can be detected using signal processing techniques. More recent advances have also shown that connectivity and network-level effects can provide critical information that complements the classical brain regional perspective. Thus, the modern toolkit for epilepsy electrophysiology now includes complex systems approaches such as network science (e.g., graph theory), nonlinear signal processing, information theory, and machine learning techniques. Complex systems approaches have made their contribution to our understanding of epilepsy and to the development of new tools that might improve its diagnosis and treatment.

Information

Type: Chapter
Information: A Complex Systems Approach to Epilepsy
Concept, Practice, and Therapy
, pp. 99 - 126

DOI: https://doi.org/10.1017/9781108582285.008 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Rosenow, , and , F., Lüders, H. Presurgical evaluation of epilepsy. Brain, 124(9), 1683–700, (2001).Google Scholar

Talairach, J., Bancaud, J., Szikla, G., et al. Approche nouvelle de la neurochirurgie de l’épilepsie. Méthodologie stéréotaxique et résultats thérapeutiques. 1. Introduction et historique [New approach to the neurosurgery of epilepsy. Stereotaxic methodology and therapeutic results. 1. Introduction and history]. Neurochirurgie, 20 Suppl 1, 1–240 (1974).Google Scholar

Munari, C., and Bancaud, J. The role of stereo-EEG in the evaluation of partial epileptic seizures. In The Epilepsies. London: Butterworths. 1985. p. 267–306.Google Scholar

Guenot, M., Isnard, J., Ryvlin, P., et al. Neurophysiological monitoring for epilepsy surgery: The Talairach SEEG method. Stereotact. Funct. Neurosurg., 77(1–4), 29–32 (2001).Google Scholar

Kahane, P., Minotti, L., Hoffmann, D., Lachaux, J.-P., Ryvlin, P. Invasive EEG in the definition of the seizure onset zone: depth electrodes. Handb. Clin. Neurophysiol., 1(3), 109–33 (2003).CrossRef Google Scholar

Cossu, M., Cardinale, F., Castana, L., et al. Stereoelectroencephalography in the presurgical evaluation of focal epilepsy: A retrospective analysis of 215 procedures. Neurosurgery, 57(4), 706–18 (2005).Google Scholar

Engel, A. K., Moll, C. K. E., Fried, I., and Ojemann, G. A. Invasive recordings from the human brain: clinical insights and beyond. Nat. Rev. Neurosci., 6(1), 35–47 (2005).Google Scholar

Cardinale, F., Cossu, M., Castana, L., et al. Stereoelectroencephalography: Surgical methodology, safety, and stereotactic application accuracy in 500 procedures. Neurosurgery., 72(3), 353–66 (2013).Google Scholar

Chauvel, P., Buser, P., Badier, J., et al. La “zone épileptogène” chez l’homme: représentation des événements intercritiques par cartes spatio-temporelles [The "epileptogenic zone" in humans: Representation of intercritical events by spatio-temporal maps]. Rev. Neurol., 143, 443–50 (1987).Google Scholar

Bancaud, J., Angelergues, R., Bernouilli, C., et al. Functional stereotaxic exploration (SEEG) of epilepsy. Electroencephalogr. Clin. Neurophysiol., 28(1), 85–6 (1970).Google Scholar

Bartolomei, F., Chauvel, P., and Wendling, F. Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG. Brain, 131(7), 1818–30 (2008).Google Scholar

Cohen, M. X. Analyzing Neural Time Series Data: Theory and practice. Cambridge: MIT press. 2014.Google Scholar

Worrell, G. A., Parish, L., Cranstoun, S. D., et al. High-frequency oscillations and seizure generation in neocortical epilepsy. Brain, 127(7), 1496–506 (2004).CrossRef Google Scholar PubMed

Aubert, S., Wendling, F., Regis, J., et al. Local and remote epileptogenicity in focal cortical dysplasias and neurodevelopmental tumours. Brain, 132(11), 3072–86 (2009).CrossRef Google Scholar PubMed

David, O., Blauwblomme, T., Job, A.-S., et al. Imaging the seizure onset zone with stereo-electroencephalography. Brain, 134(10), 2898–911 (2011).Google Scholar

Geertsema, E. E., Visser, G. H., Velis, D. N., et al. Automated seizure onset zone approximation based on nonharmonic high-frequency oscillations in human interictal intracranial EEGs. Int. J. Neural. Syst., 25(05), 1550015 (2015).Google Scholar

Liu, S., Sha, Z., Sencer, A., et al. Exploring the time-frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy. J. Neural. Eng., 13(2), 26026 (2016).CrossRef Google Scholar PubMed

Murphy, P. M., von Paternos, A. J., and Santaniello, S. A novel HFO-based method for unsupervised localization of the seizure onset zone in drug-resistant epilepsy. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.,. 2017, 1054–7. (2017).Google Scholar

Jefferys, J. G. R., Menendez de la Prida, L., Wendling, F., et al. Mechanisms of physiological and epileptic HFO generation. Prog. Neurobiol., 98(3), 250–64 (2012).Google Scholar

Perucca, P., Dubeau, F., and Gotman, J. Intracranial electroencephalographic seizure-onset patterns: Effect of underlying pathology. Brain, 137(1), 183–96 (2014).Google Scholar

Lagarde, S., Bonini, F., McGonigal, A., et al. Seizure-onset patterns in focal cortical dysplasia and neurodevelopmental tumors: Relationship with surgical prognosis and neuropathologic subtypes. Epilepsia, 57(9), 1426–35 (2016).Google Scholar

Singh, S., Sandy, S., and Wiebe, S. Ictal onset on intracranial EEG: Do we know it when we see it? State of the evidence. Epilepsia, 56(10), 1629–38 (2015).CrossRef Google Scholar PubMed

Gnatkovsky, V., Francione, S., Cardinale, F., et al. Identification of reproducible ictal patterns based on quantified frequency analysis of intracranial EEG signals. Epilepsia, 52(3), 477–88 (2011).Google Scholar

Vila-Vidal, M., Principe, A., Ley, M., et al. Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification. Clin. Neurophysiol., 128(6), 977–85 (2017).Google Scholar

Vila-Vidal, M. Epylib v1.0, 2019. github.com/mvilavidal/Epylib, Zenodo, doi:10.5281/ZENODO.2630604CrossRef Google Scholar

Vila-Vidal, M., Pérez Enríquez, C., Principe, A., et al. Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction. Neuroimage, 208, 707497 (2020).CrossRef Google Scholar PubMed

Gnatkovsky, V., de, Curtis, M., Pastori, C., et al. Biomarkers of epileptogenic zone defined by quantified stereo-EEG analysis. Epilepsia, 55(2), 296–305 (2014).Google Scholar

Andrzejak, R. G., Schindler, K., and Rummel, C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 86(4 Pt 2), 046206 (2012)CrossRef Google Scholar PubMed

Andrzejak, R. G., David, O., Gnatkovsky, V., et al. Localization of epileptogenic zone on pre-surgical intracranial EEG recordings: Toward a validation of quantitative signal analysis approaches. Brain Topogr., 28(6), 832–7 (2015).Google Scholar

Bartolomei, F., Wendling, F., and Chauvel, P. The concept of an epileptogenic network in human partial epilepsies. Neurochirurgie, 54(3), 174–84 (2008).Google Scholar

Sporns, O., Chialvo, D. R., Kaiser, M., and Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn. Sci., 8(9), 418–25 (2004).Google Scholar

van Mierlo, P., Papadopoulou, M., Carrette, E., et al. Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization. Prog. Neurobiol., 121, 19–35 (2014).Google Scholar

Friston, K. J. Functional and effective connectivity: A review. Brain Connect, 1(1), 13–36 (2011).CrossRef Google Scholar PubMed

Bullmore, E., and Sporns, O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci., 10(3), 186–98 (2009).Google Scholar

van Mierlo, P., Carrette, E., Hallez, H., et al. Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals. Neuroimage, 56(3), 1122–33 (2011).CrossRef Google Scholar PubMed

Gersch, W., and Goddard, G. V. Epileptic focus location: Spectral analysis method. Science, 169(3946), 701–2 (1970).Google Scholar

Brazier, M. A. B. Spread of seizure discharges in epilepsy: Anatomical and electrophysiological considerations. Exp. Neurol., 36(2), 263–72 (1972).Google Scholar

Gotman, J. Measurement of small time differences between EEG channels: Method and application to epileptic seizure propagation. Electroencephalogr. Clin. Neurophysiol., 56(5), 501–14 (1983).Google Scholar

Lütkepohl, H. New Introduction to Multiple Time Series Analysis. Berlin: Springer Science & Business Media. 2005.Google Scholar

Baccalá, L. A., and Sameshima, K. Partial directed coherence: A new concept in neural structure determination. Biol. Cybern., 84(6), 463–74 (2001).Google Scholar

Kaminski, M. J., and Blinowska, K. J. A new method of the description of the information flow in the brain structures. Biol. Cybern., 65(3), 203–10 (1991).CrossRef Google Scholar PubMed

Astolfi, L., Cincotti, F., Mattia, D., et al. Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators. IEEE Trans. Biomed. Eng., 55(3), 902–13 (2008).Google Scholar

Wilke, C., Ding, Lei, and He, Bin. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. IEEE Trans. Biomed. Eng., 55(11), 2557–64 (2008).Google Scholar

Varotto, G., Tassi, L., Franceschetti, S., Spreafico, R., and Panzica, F. Epileptogenic networks of type II focal cortical dysplasia: A stereo-EEG study. Neuroimage., 61(3), 591–8 (2012).Google Scholar

Franaszczuk, P. J., Bergey, G. K., and Kamiński, M. J. Analysis of mesial temporal seizure onset and propagation using the directed transfer function method. Electroencephalogr. Clin. Neurophysiol., 91(6), 413–27 (1994).CrossRef Google Scholar PubMed

Wilke, C., van Drongelen, W., Kohrman, M., and He, B. Identification of epileptogenic foci from causal analysis of ECoG interictal spike activity. Clin. Neurophysiol., 120(8), 1449–56 (2009).Google Scholar

Kim, J. S., Im, C. H., Jung, Y. J., et al. Localization and propagation analysis of ictal source rhythm by electrocorticography. Neuroimage, 52(4), 1279–88 (2010).Google Scholar

Kim, J.-Y., Kang, H.-C., Cho, J.-H., et al. Combined use of multiple computational intracranial EEG analysis techniques for the localization of epileptogenic zones in Lennox–Gastaut syndrome. Clin. EEG Neurosci., 45(3), 169–78 (2014).Google Scholar

van Mierlo, P., Carrette, E., Hallez, H., et al. Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy. Epilepsia, 54(8), 1409–18 (2013).CrossRef Google Scholar PubMed

Wilke, C., Worrell, G., and He, B. Graph analysis of epileptogenic networks in human partial epilepsy. Epilepsia, 52(1), 84–93 (2011).Google Scholar

Andrzejak, R. G., Chicharro, D., Lehnertz, K., and Mormann, F. Using bivariate signal analysis to characterize the epileptic focus: The benefit of surrogates. Phys. Rev. E, 83(4), 046203 (2011).Google Scholar

Sakkalis, V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med., 41(12), 1110–7 (2011).Google Scholar

Panzica, F., Varotto, G., Rotondi, F., Spreafico, R., and Franceschetti, S. Identification of the epileptogenic zone from stereo-EEG signals: A connectivity-graph theory approach. Front. Neurol., 4, 175 (2013).Google Scholar

Lachaux, J. P., Rodriguez, E., Martinerie, J., and Varela, F. J. Measuring phase synchrony in brain signals. Hum. Brain Mapp., 8(4), 194–208 (1999).Google Scholar

Mormann, F., Lehnertz, K., David, P. E. and Elger, C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys. D Nonlinear Phenom. 144(3–4), 358–69 (2000).Google Scholar

Stam, C. J., and Reijneveld, J. C. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed. Phys., 1(1), 3 (2007).Google Scholar

van Diessen, E., Diederen, S. J. H., Braun, K. P. J., Jansen, F. E., and Stam, C. J. Functional and structural brain networks in epilepsy: What have we learned? Epilepsia, 54(11), 1855–65 (2013).Google Scholar

Pijn, J. P., and Lopes da Silva, F. Propagation of electrical activity: nonlinear associations and time delays between eeg signals. In Basic Mechanisms of the EEG. Boston, MA: Birkhäuser Boston. 1993. p. 41–61.Google Scholar

Wendling, F., Bartolomei, F., Bellanger, J. J., and Chauvel, P. Identification de réseaux épileptogènes par modélisation et analyse non linéaire des signaux SEEG. Neurophysiol. Clin. Neurophysiol., 31(3), 139–51 (2001).Google Scholar

Wendling, F., Chauvel, P., Biraben, A., and Bartolomei, F. From intracerebral EEG signals to brain connectivity: Identification of epileptogenic networks in partial epilepsy. Front. Syst. Neurosci., 25(4), 154 (2010).Google Scholar

Balatskaya, A., Roehri, N., Lagarde, S., et al. The “Connectivity Epileptogenicity Index” (cEI), a method for mapping the different seizure onset patterns in StereoElectroEncephalography recorded seizures. Clin. Neurophysiol., 131(8), 1947–55 (2020).Google Scholar

Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948).Google Scholar

AdTau, and Vila-Vidal, M. DI-Inference-for-Python. 2020. doi:10.5281/zenodo.4067039Google Scholar

AdTau. AdTau/DI-Inference v1.0 (v1.0). Zenodo. 2020. https://doi.org/10.5281/zenodo.4059445 CrossRef Google Scholar

Mars, N. J. I., Thompson, P. M., and Wilkus, R. J. Spread of epileptic seizure activity in humans. Epilepsia, 26(1), 85–94 (1985).Google Scholar

Sabesan, S., Good, L. B., Tsakalis, K. S., et al. Information flow and application to epileptogenic focus localization from intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng., 17(3), 244–53 (2009).Google Scholar

Malladi, R., Kalamangalam, G., Tandon, N., and Aazhang, B. Identifying seizure onset zone from the causal connectivity inferred using directed information. IEEE J. Sel. Top. Signal. Process., 10(7), 1267–83 (2016).Google Scholar

Plummer, C., Vogrin, S. J., Woods, W. P., et al. Interictal and ictal source localization for epilepsy surgery using high-density EEG with MEG: A prospective long-term study. Brain, 142(4), 932–51 (2019).Google Scholar

van Mierlo, P., Höller, Y., Focke, N. K., and Vulliemoz, S. Network perspectives on epilepsy using EEG/MEG source connectivity. Front. Neurol., 10, 721 (2019).Google Scholar

Centeno, M., and Carmichael, D. W. Network connectivity in epilepsy: Resting state fMRI and EEG–fMRI contributions. Front,. Neurol., 5, 93 (2014).CrossRef Google Scholar PubMed

Lei, X., Wu, T., and Valdes-Sosa, P. Incorporating priors for EEG source imaging and connectivity analysis. Front. Neurosci., 9, 284 (2015).Google Scholar

Staljanssens, W., Strobbe, G., Van, Holen, R., et al. EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy. Neuroimage Clin., 16, 689–98 (2017).CrossRef Google Scholar PubMed

Pellegrino, G., Hedrich, T., Chowdhury, R., et al. Source localization of the seizure onset zone from ictal EEG/MEG data. Hum. Brain Mapp., 37(7), 2528–46 (2016).Google Scholar

Ding, L., Worrell, G. A., Lagerlund, T. D., and He, B. Ictal source analysis: Localization and imaging of causal interactions in humans. Neuroimage, 34(2), 575–86 (2007).Google Scholar

Lu, Y., Yang, L., Worrell, G. A., and He, B. Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients. Clin. Neurophysiol., 123(7), 1275–83 (2012).Google Scholar

Elshoff, L., Muthuraman, M., Anwar, A. R., et al. Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures. PLoS One, 8(10), e78422 (2013).Google Scholar

Clemens, B., Puskás, S., Bessenyei, M., et al. EEG functional connectivity of the intrahemispheric cortico-cortical network of idiopathic generalized epilepsy. Epilepsy Res., 96(1), 11–23 (2011).Google Scholar

Staljanssens, W., Strobbe, G., Van Holen, R., et al. Seizure onset zone localization from ictal high-density EEG in refractory focal epilepsy. Brain Topogr., 30(2), 257–71 (2017).Google Scholar

Mormann, F., Andrzejak, R. G., Elger, C. E., and Lehnertz, K. Seizure prediction: The long and winding road. Brain, 130, 314–33 (2007).Google Scholar

Hughes, J., Devinsky, O., Feldmann, E., and Bromfield, E. Premonitory symptoms in epilepsy. Seizure, 2(3), 201–3 (1993).Google Scholar

Schulze-Bonhage, A., Kurth, C., Carius, A., Steinhoff, B. J., and Mayer, T. Seizure anticipation by patients with focal and generalized epilepsy: A multicentre assessment of premonitory symptoms. Epilepsy Res., 70(1), 83–8 (2006).Google Scholar

Khambhati, A. N., Bassett, D. S., Oommen, B. S., et al. Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy. eNeuro, 4(1), ENEURO.0091–16.2017 (2017).CrossRef Google Scholar PubMed

Tauste Campo, A., Principe, A., Ley, M., Rocamora, R., and Deco, G. Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain. PLoS Biol., 16(4), e2002580 (2018).Google Scholar

Iasemidis, L. D., Shiau, Deng-Shan., Chaovalitwongse, W., et al. Adaptive epileptic seizure prediction system. IEEE Trans, Biomed. Eng., 50(5), 616–27 (2003).Google Scholar

Viglione, S S., and Walsh, G. O. Proceedings: Epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol., 39(4), 435–6 (1975).Google Scholar

Lange, H. H., Lieb, J. P., Engel, J., and Crandall, P. H. Temporo-spatial patterns of pre-ictal spike activity in human temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol., 56(6), 543–55 (1983).Google Scholar

Iasemidis, L. D., Sackellares, J. C., Zaveri, H. P., and Williams, W. J. Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr., 2(3), 187–201 (1990).Google Scholar

Lehnertz, K., and Elger, C. E. Can Epileptic Seizures be Predicted? Evidence from Nonlinear Time Series Analysis of Brain Electrical Activity. Phys. Rev. Lett., 80(22), 5019–22 (1998).Google Scholar

Le Van Quyen, M., Soss, J., Navarro, V., et al. Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin. Neurophysiol., 116(3), 559–68 (2005).CrossRef Google Scholar PubMed

Mormann, F., Kreuz, T., Andrzejak, R. G., et al. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res., 53(3), 173–85 (2003).Google Scholar

da Silva, F. L., Blanes, W., Kalitzin, SN., et al. Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity. Epilepsia, 44(s12), 72–83 (2003).Google Scholar

Jirsa, V. K., Stacey, W. C., Quilichini, P. P., Ivanov, A. I., and Bernard, C. On the nature of seizure dynamics. Brain, 137(8), 2210–30 (2014).Google Scholar

Stam, C. J. Modern network science of neurological disorders. Nat. Rev. Neurosci., 15(10), 683–95 (2014).Google Scholar

Ponten, S. C., Bartolomei, F., and Stam, C. J. Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin. Neurophysiol., 118(4), 918–27 (2007).Google Scholar

Kramer, M. A., Kolaczyk, E. D., and Kirsch, H. E. Emergent network topology at seizure onset in humans. Epilepsy Res., 79(2–3), 173–86 (2008).CrossRef Google Scholar PubMed

Geier, C., Lehnertz, K., and Bialonski, S. Time-dependent degree-degree correlations in epileptic brain networks: From assortative to dissortative mixing. Front. Hum. Neurosci., 20(9), 462 (2015).Google Scholar

Takahashi, H., Takahashi, S., Kanzaki, R., and Kawai, K. State-dependent precursors of seizures in correlation-based functional networks of electrocorticograms of patients with temporal lobe epilepsy. Neurol. Sci., 33(6), 1355–64 (2012).Google Scholar

Karoly, P. J., Ung, H., Grayden, D. B., et al. The circadian profile of epilepsy improves seizure forecasting. Brain, 140(8), 2169–82 (2017).Google Scholar

Cook, M. J., O’Brien, T. J., Berkovic, S. F., et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol., 12(6), 563–71 (2013).Google Scholar

Park, Y., Luo, L., Parhi, K. K., and Netoff, T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia, 52(10), 1761–70 (2011).Google Scholar

Gadhoumi, K., Lina, J.-M., Mormann, F., and Gotman, J. Seizure prediction for therapeutic devices: A review. J. Neurosci. Methods, 260, 270–82 (2016).Google Scholar

Freestone, D. R., Karoly, P. J., and Cook, M. J. A forward-looking review of seizure prediction. Curr. Opin. Neurol., 30(2), 167–73 (2017).Google Scholar

Kiral-Kornek, I., Roy, S., Nurse, E., et al. Epileptic seizure prediction using big data and deep learning: Toward a mobile system. EBioMedicine, 1(27), 103–11 (2018).Google Scholar

Brinkmann, B. H., Wagenaar, J., Abbot, D., et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain, 139(6), 1713–22 (2016).Google Scholar

Beniczky, S., Karoly, P., Nurse, E., Ryvlin, P., and Cook, M. Machine learning and wearable devices of the future. Epilepsia, 62(Suppl. 2), S116–24 (2021).Google Scholar

Acharya, U. R., Hagiwara, Y., and Adeli, H. Automated seizure prediction. Epilepsy Behav., 88, 251–61 (2018).CrossRef Google Scholar PubMed

Smith, G. C., and Stacey, W. C. Graph theory for EEG: Can we learn to trust another black box? Brain, 142(12), 3663–6 (2019).Google Scholar

Kini, L. G., Bernabei, J. M., Mikhail, F., et al. Virtual resection predicts surgical outcome for drug-resistant epilepsy. Brain, 142(12), 3892–905 (2019).Google Scholar

Mtui, E., Gruener, G., and Dockery, P. Chapter 30. Electroencephalography. In Fitzgerald’s Clinical Neuroanatomy and Neuroscience. 7th ed. Philadelphia: Elsevier. 2016. p. 289–97.Google Scholar

Javidan, M. Electroencephalography in mesial temporal lobe epilepsy: A review. Epilepsy Res. Treat., 2012, 1–17 (2012).Google Scholar

Lindsay, K. W., Bone, I., and Fuller, G. Section, IV. Localised neurological disease and its management: A. Intracranial. In Neurology and Neurosurgery Illustrated. 5th ed. Churchill Livingstone; 2011. p. 217–388.Google Scholar

Granger, C. W. J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424 (1969).Google Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the HTML of this chapter is currently unknown and may be updated in the future.