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Electroencephalogram

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Abstract

The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing metal electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. This chapter overviews the fundamental basis of the EEG, the typical signals that are produced and how they are collected and analysed. Significant attention is given to reviewing the state of the art in EEG collection in both electrode designs and instrumentation hardware. In particular, recent developments in ear-EEG and in conformal tattoo electrodes for very long-term monitoring are highlighted. The chapter concludes by overviewing the applications of EEG technology in medical and non-medical domains, demonstrating the emergence of “consumer neuroscience” applications as EEG devices become more available and more readily useable by non-specialist operators.

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References

  1. Berger, H. (1929). Uber das eletrenkephalogram des menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527–570.

    Article  Google Scholar 

  2. Buzsaki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13(6), 407–420.

    Article  Google Scholar 

  3. Lopes da Silva, F. (2009). EEG: Origin and measurement. In C. Mulert & L. Lemieux (Eds.), EEG – fMRI (pp. 19–38). Heidelberg: Springer.

    Chapter  Google Scholar 

  4. Jackson, A. F., & Bolger, D. J. (2014). The neurophysiological bases of EEG and EEG measurement: A review for the rest of us. Psychophysiology, 51(11), 1061–1071.

    Article  Google Scholar 

  5. Krauss, G. L., & Fisher, R. S. (2006). The Johns Hopkins atlas of digital EEG: An interactive training guide. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  6. Lal, S. K., & Craig, A. (2002). Driver fatigue: Electroencephalography and psychological assessment. Psychophysiology, 39(3), 313–321.

    Article  Google Scholar 

  7. Curio, G. (2000). Ain’t no rhythm fast enough: EEG bands beyond beta. Journal of Clinical Neurophysiology, 17(4), 339–340.

    Article  Google Scholar 

  8. Binnie, C. D., Rowan, A. J., & Gutter, T. (1982). A manual of electroencephalographic technology. Cambridge: Cambridge University Press.

    Google Scholar 

  9. Noachtar, S., Binnie, C., Ebersole, J., Mauguiere, F., Sakamoto, A., & Westmoreland, B. (1999). A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the EEG findings. In G. Deuschl & A. Eisen (Eds.), Recommendations for the practice of clinical neurophysiology: Guidelines of the international federation of clinical physiology, Electroencephalography and clinical neurophysiology supplement (Vol. 52, 2nd ed., pp. 21–41). Amsterdam: Elsevier.

    Google Scholar 

  10. Celesia, G. G., & Chen, R.-C. (1976). Parameters of spikes in human epilepsy. Diseases of the Nervous System, 37(5), 277–281.

    Google Scholar 

  11. Massimini, M., Huber, R., Ferrarelli, F., Hill, S., & Tononi, G. (2004). The sleep slow oscillation as a traveling wave. The Journal of Neuroscience, 24(31), 6862–6870.

    Article  Google Scholar 

  12. Muller-Putz, G. R., Scherer, R., Brauneis, C., & Pfurtscheller, G. (2005). Steady-state visual evoked potential (SSVEP)-based communication: Impact of harmonic frequency components. Journal of Neural Engineering, 2(4), 123–130.

    Article  Google Scholar 

  13. Lins, O. G., & Picton, T. W. (1995). Auditory steady-state responses to multiple simultaneous stimuli. Electroencephalography and Clinical Neurophysiology, 96(5), 420–432.

    Article  Google Scholar 

  14. Truccolo, W. A., Ding, M., Knuth, K. H., Nakamura, R., & Bressler, S. L. (2002). Trial-to-trial variability of cortical evoked responses: Implications for the analysis of functional connectivity. Clinical Neurophysiology, 113(2), 206–226.

    Article  Google Scholar 

  15. Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010). BCI demographics: How many (and what kinds of) people can use an SSVEP BCI? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(2), 107–116.

    Article  Google Scholar 

  16. Wang, Y., Gao, S., & Gao, X. (2005). Common spatial pattern method for channel selection in motor imagery based brain-computer interface. IEEE Engineering in Medicine and Biology Society, 5, 5392–5395.

    Google Scholar 

  17. Ebner, A., Sciarretta, G., Epstein, C. M., & Nuwer, M. (1999). EEG instrumentation. In G. Deuschl & A. Eisen (Eds.), Recommendations for the practice of clinical neurophysiology: Guidelines of the international federation of clinical physiology, Electroencephalography and clinical neurophysiology supplement (Vol. 52, 2nd ed., pp. 7–10). Amsterdam: Elsevier.

    Google Scholar 

  18. Klem, G. H., Luders, H. O., Jasper, H. H., & Elger, C. (1999). The ten-twenty electrode system of the international federation. In G. Deuschl & A. Eisen (Eds.), Recommendations for the practice of clinical neurophysiology: Guidelines of the international federation of clinical physiology, Electroencephalography and clinical neurophysiology supplement (Vol. 52, 2nd ed., pp. 3–6). Amsterdam: Elsevier.

    Google Scholar 

  19. Martz, G. U., Hucek, C., & Quigg, M. (2009). Sixty day continuous use of subdermal wire electrodes for EEG monitoring during treatment of status epilepticus. Neurocritical Care, 11(2), 223–227.

    Article  Google Scholar 

  20. Webster, J. G. (1984). Reducing motion artifacts and interference in biopotential recording. IEEE Transactions on Biomedical Engineering, 31(12), 823–826.

    Article  Google Scholar 

  21. Nuwer, M. R., Comi, G., Emerson, R., Fuglsang-Frederiksen, A., Guerit, J.-M., Hinrichs, H., Ikeda, A., Luccas, F. J. C., & Rappelsberger, P. (1999). IFCN standards for digital recording of clinical EEG. In G. Deuschl & A. Eisen (Eds.), Recommendations for the practice of clinical neurophysiology: Guidelines of the international federation of clinical physiology, Electroencephalography and clinical neurophysiology supplement (Vol. 52, 2nd ed., pp. 11–14). Amsterdam: Elsevier.

    Google Scholar 

  22. Wilson, S. B., & Emerson, R. (2002). Spike detection: A review and comparison of algorithms. Clinical Neurophysiology, 113(12), 1873–1881.

    Article  Google Scholar 

  23. Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. Cambridge, MA: MIT Press.

    Google Scholar 

  24. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.

    Article  Google Scholar 

  25. Gwin, J. T., Gramann, K., Makeig, S., & Ferris, D. P. (2011). Electrocortical activity is coupled to gait cycle phase during treadmill walking. NeuroImage, 54(2), 1289–1296.

    Article  Google Scholar 

  26. Wagner, J., Solis-Escalante, T., Grieshofer, P., Neuper, C., Muller-Putz, G., & Scherer, R. (2012). Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. NeuroImage, 63(3), 1203–1211.

    Article  Google Scholar 

  27. Kohli, S., & Casson, A. J. (2015). Towards out-of-the-lab EEG in uncontrolled environments: Feasibility study of dry EEG recordings during exercise bike riding. IEEE Engineering in Medicine and Biology Society, 2015, 1025–1028.

    Google Scholar 

  28. Zink, R., Hunyadi, B., Van Huffel, S., & De Vos, M. (2016). Mobile EEG on the bike: Disentangling attentional and physical contributions to auditory attention tasks. Journal of Neural Engineering, 13(4), 046017.

    Article  Google Scholar 

  29. Mijovic, B., De Vos, M., Gligorijevic, I., Taelman, J., & Van Huffel, S. (2010). Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Transactions on Biomedical Engineering, 57(9), 2188–2196.

    Article  Google Scholar 

  30. Logesparan, L., Casson, A. J., & Rodriguez-Villegas, E. (2012). Optimal features for online seizure detection. Medical & Biological Engineering & Computing, 50(7), 659–669.

    Article  Google Scholar 

  31. Micheloyannis, S., Flitzanis, N., Papanikolaou, E., Bourkas, M., Terzakis, D., Arvanitis, S., & Stam, C. J. (1998). Usefulness of non-linear EEG analysis. Acta Neurologica Scandinavica, 97(1), 13–19.

    Article  Google Scholar 

  32. Mallat, S. (1999). A wavelet tour of signal processing (2nd ed.). San Diego: Academic.

    MATH  Google Scholar 

  33. Jentzsch, I., & Sommer, W. (2001). Sequence-sensitive subcomponents of P300: Topographical analyses and dipole source localization. Psychophysiology, 38(4), 607–621.

    Article  Google Scholar 

  34. Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441–446.

    Article  Google Scholar 

  35. Townsend, G., Graimann, B., & Pfurtscheller, G. (2004). Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(2), 258–265.

    Article  Google Scholar 

  36. LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013). Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. Journal of Neural Engineering, 10(4), 046003.

    Article  Google Scholar 

  37. Gotman, J., & Gloor, P. (1976). Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalography and Clinical Neurophysiology, 41(5), 513–529.

    Article  Google Scholar 

  38. Pollock, V. E., Schneider, L. S., & Lyness, S. A. (1990). EEG amplitudes in healthy, late-middle-aged and elderly adults: Normality of the distributions and correlations with age. Electroencephalography and Clinical Neurophysiology, 75(4), 276–288.

    Article  Google Scholar 

  39. Casson, A. J., & Rodriguez-Villegas, E. (2011). Interfacing biology and circuits: Quantification and performance metrics. In K. Iniewski (Ed.), CMOS biomicrosystems: Where electronics meet biology (pp. 3–32). Hoboken: Wiley.

    Google Scholar 

  40. Christensen, J. C., Estepp, J. R., Wilson, G. F., & Russell, C. A. (2011). The effects of day-to-day variability of physiological data on operator functional state classification. NeuroImage, 59(1), 57–63.

    Article  Google Scholar 

  41. Tallgren, P., Vanhatalo, S., Kaila, K., & Voipio, J. (2005). Evaluation of commercially available electrodes and gels for recording of slow EEG potentials. Clinical Neurophysiology, 116(4), 799–806.

    Article  Google Scholar 

  42. Neuman, M. R. (2000). Biopotential electrodes. In J. D. Bronzino (Ed.), The biomedical engineering handbook (2nd ed.). Boca Raton: CRC Press.

    Google Scholar 

  43. Huigen, E., Peper, A., & Grimbergen, C. A. (2002). Investigation into the origin of the noise of surface electrodes. Medical & Biological Engineering & Computing, 40(3), 332–338.

    Article  Google Scholar 

  44. Xu, J., Yazicioglu, R. F., Grundlehner, B., Harpe, P., Makinwa, K. A. A., & Van Hoof, C. (2011). A 160 μW 8-channel active electrode system for EEG monitoring. IEEE Transactions on Biomedical Circuits and System, 5(6), 555–567.

    Article  Google Scholar 

  45. Ferree, T. C., Luu, P., Russell, G. S., & Tucker, D. M. (2001). Scalp electrode impedance, infection risk, and EEG data quality. Clinical Neurophysiology, 112(3), 536–544.

    Article  Google Scholar 

  46. Krachunov, S., & Casson, A. J. (2016). 3D printed dry EEG electrodes. Sensors, 16(10), 1635.

    Article  Google Scholar 

  47. Taheri, B. A., Knight, R. T., & Smith, R. L. (1994). A dry electrode for EEG recording. Electroencephalography and Clinical Neurophysiology, 90(5), 376–383.

    Article  Google Scholar 

  48. Chi, Y. M., Jung, T. P., & Cauwenberghs, G. (2010). Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Reviews in Biomedical Engineering, 3(1), 106–119.

    Article  Google Scholar 

  49. Casson, A. J. (2016, August). An introduction to next generation EEG electrodes. IEEE EMBC. Orlando: IEEE.

    Google Scholar 

  50. Lopez-Gordo, M. A., Sanchez-Morillo, D., & Pelayo Valle, F. (2014). Dry EEG electrodes. Sensors, 14(7), 12847–12870.

    Article  Google Scholar 

  51. Grass Technologies. http://www.grasstechnologies.com/. Accessed Jan 2017.

  52. Debener, S., Emkes, R., De Vos, M., & Bleichner, M. (2015). Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Scientific Reports, 5(16743), 1–11.

    Google Scholar 

  53. Smith, P. E. M., & Wallace, S. J. (2001). Clinicians’ guide to epilepsy. London: Arnold.

    Google Scholar 

  54. Waterhouse, E. (2003). New horizons in ambulatory electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 22(3), 74–80.

    Article  Google Scholar 

  55. Smith, S. J. M. (2005). EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Neurology, Neurosurgery, and Psychiatry, 76(2), ii2–ii7.

    Google Scholar 

  56. Ebersole, J. S., & Bridgers, S. L. (1985). Direct comparison of 3- and 8-channel ambulatory cassette EEG with intensive inpatient monitoring. Neurology, 35(6), 846–854.

    Article  Google Scholar 

  57. Casson, A. J., Yates, D. C., Smith, S. J. M., Duncan, J. S., & Rodriguez-Villegas, E. (2010). Wearable electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 29(3), 44–56.

    Article  Google Scholar 

  58. Emotiv. https://www.emotiv.com/. Accessed Jan 2017.

  59. Muse. http://www.choosemuse.com/. Accessed Jan 2017.

  60. Neurosky. http://neurosky.com/. Accessed Jan 2017.

  61. Rythm. https://rythm.co/. Accessed Jan 2017.

  62. Kokoon. https://kokoon.io/. Accessed Jan 2017.

  63. Badcock, N. A., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., & McArthur, G. (2013). Validation of the Emotiv EPOC (R) EEG gaming system for measuring research quality auditory ERPs. PeerJ, 19(1), e38.

    Article  Google Scholar 

  64. OpenBCI. http://openbci.com/. Accessed Jan 2017.

  65. Mihajlovic, V., Grundlehner, B., Vullers, R., & Penders, J. (2015). Wearable, wireless EEG solutions in daily life applications: What are we missing? IEEE Journal of Biomedical and Health Informatics, 19(1), 6–21.

    Article  Google Scholar 

  66. Lin, C. T., Liao, L. D., Liu, Y. H., Wang, I. J., Lin, B. S., & Chang, J. Y. (2011). Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Transactions on Biomedical Engineering, 58(5), 1200–1207.

    Article  Google Scholar 

  67. Looney, D., Kidmose, P., Park, C., Ungstrup, M., Rank, M. L., Rosenkranz, K., & Mandic, D. (2012). The in-the-ear recording concept: User-centered and wearable brain monitoring. IEEE Pulse, 3(6), 32–42.

    Article  Google Scholar 

  68. Kidmose, P., Looney, D., Ungstrup, M., Rank, M. L., & Mandic, D. P. (2013). A study of evoked potentials from ear-EEG. IEEE Transactions on Biomedical Engineering, 60(10), 2824–2830.

    Article  Google Scholar 

  69. Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., Yu, K. J., Kim, T., Chowdhury, R., Ying, M., Xu, L., Li, M., Chung, H.-J., Keum, H., McCormick, M., Liu, P., Zhang, Y.-W., Omenetto, F. G., Huang, Y., Coleman, T., & Rogers, J. A. (2011). Epidermal electronics. Science, 333(6044), 838–843.

    Article  Google Scholar 

  70. Norton, J. J., Lee, D. S., Lee, J. W., Lee, W., Kwon, O., Won, P., Jung, S. Y., Cheng, H., Jeong, J. W., Akce, A., Umunna, S., Na, I., Kwon, Y. H., Wang, X. Q., Liu, Z., Paik, U., Huang, Y., Bretl, T., Yeo, W. H., & Rogers, J. A. (2015). Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface. Proceedings of the National Academy of Sciences of the United States of America, 112(13), 3920–3925.

    Article  Google Scholar 

  71. Batchelor, J. C., Yeates, S. G., & Casson, A. J. (2016). Conformal electronics for longitudinal bio-sensing in at-home assistive and rehabilitative devices. IEEE Engineering in Medicine and Biology Society, 2016, 3159–3162.

    Google Scholar 

  72. Sanchez-Romaguera, V., Ziai, M. A., Oyeka, D., Barbosa, S., Wheeler, J. S. R., Batchelor, J. C., Parker, E. A., & Yeates, S. G. (2013). Towards inkjet-printed low cost passive UHF RFID skin mounted tattoo paper tags based on silver nanoparticle inks. Journal of Materials Chemistry C, 1(39), 6395–6402.

    Article  Google Scholar 

  73. Ziai, M. A., & Batchelor, J. C. (2011). Temporary on-skin passive UHF RFID transfer tag. IEEE Transactions on Antennas and Propagation, 59(10), 3565–3571.

    Article  Google Scholar 

  74. Iber, C., Ancoli-Israel, S., Chesson, A., & Quan, S. F. (2007). The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications. Westchester: American Academy of Sleep Medicine.

    Google Scholar 

  75. Neligan, A., & Sander, J. W. (2015). The incidence and prevalence of epilepsy. Available https://www.epilepsysociety.org.uk/. Accessed Jan 2017.

  76. Browne, T. R., & Holmes, G. L. (2001). Epilepsy. The New England Journal of Medicine, 344(15), 1145–1151.

    Article  Google Scholar 

  77. Epilepsy society, diagnosing epilepsy. Available https://www.epilepsysociety.org.uk. Accessed Jan 2017.

  78. National Institute for Clinical Excellence. (2004). NICE guidelines: The diagnosis and management of the epilepsies in adults and children in primary and secondary care. London: NICE.

    Google Scholar 

  79. Rechtschaffen, A., & Kales, A. (Eds.). (1968). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, DC: Public Health Service, U.S. Government Printing Office.

    Google Scholar 

  80. Carney, P. R., Berry, R. B., & Geyer, J. D. (Eds.). (2005). Clinical sleep disorders. Philadelphia: Lippincott Williams and Wilkins.

    Google Scholar 

  81. Colten, H. R., & Altevogt, B. M. (Eds.). (2006). Sleep disorders and sleep deprivation: An unmet public health problem. Washington, DC: National Academies Press.

    Google Scholar 

  82. Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211–1279.

    Article  Google Scholar 

  83. Ramos-Murguialday, A., Broetz, D., Rea, M., Laer, L., Yilmaz, O., Brasil, F. L., Liberati, G., Curado, M. R., Garcia-Cossio, E., Vyziotis, A., Cho, W., Agostini, M., Soares, E., Soekadar, S., Caria, A., Cohen, L. G., & Birbaumer, N. (2013). Brain-machine-interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74(1), 100–108.

    Article  Google Scholar 

  84. Neto, E., Allen, E. A., Aurlien, H., Nordby, H., & Eichele, T. (2015). EEG spectral features discriminate between Alzheimer’s and vascular dementia. Frontiers in Neurology, 6(25), 1–9.

    Google Scholar 

  85. Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology, 78(3), 252–259.

    Article  Google Scholar 

  86. Zander, T. O., & Kothe, C. (2011). Towards passive brain computer interfaces: Applying brain computer interface technology to human machine systems in general. Journal of Neural Engineering, 8(2), 025005.

    Article  Google Scholar 

  87. Carlson, T., & Millan, J. R. (2013). Brain-controlled wheelchairs: A robotic architecture. IEEE Journal of Robotics and Automation, 20(1), 65–73.

    Article  Google Scholar 

  88. Kubler, A., Mushahwar, V. K., Hochberg, L. R., & Donoghue, J. P. (2004). BCI meeting 2005—Workshop on clinical issues and applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 131–134.

    Article  Google Scholar 

  89. Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.-P., & Gao, S. (2015). High-speed spelling with a noninvasive brain–computer interface. Proceedings of the National Academy of Sciences of the United States of America, 112(44), 6058–6067.

    Article  Google Scholar 

  90. Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., & Edlinger, G. (2009). How many people are able to control a P300-based brain–computer interface (BCI)? Neuroscience Letters, 462(1), 94–98.

    Article  Google Scholar 

  91. Ekandem, J. I., Davis, T. A., Alvarez, I., James, M. T., & Gilbert, J. E. (2012). Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics, 55(5), 592–598.

    Article  Google Scholar 

  92. Dijksterhuis, C., De Waard, D., Brookhuis, K., Mulder, B., & De Jong, R. (2013). Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns. Frontiers in Neuroscience, 393(7), 149.

    Google Scholar 

  93. Casson, A. J. (2014). Artificial Neural Network classification of operator workload with an assessment of time variation and noise-enhancement to increase performance. Frontiers in Neuroscience, 8(372), 1–10.

    Google Scholar 

  94. Wilson, G. F., & Russell, C. A. (2007). Performance enhancement in a UAV task using psychophysiological determined adaptive aiding. Human Factors, 49(6), 1005–1019.

    Article  Google Scholar 

  95. Ayaz, H., Shewokis, P. A., Bunce, S., Izzetoglu, K., Willems, B., & Onaral, B. (2012). Optical brain monitoring for operator training and mental workload assessment. NeuroImage, 59(1), 36–47.

    Article  Google Scholar 

  96. Transparency market research. Available http://www.prweb.com/releases/2013/11/prweb11337791.htm. Accessed Jan 2017.

  97. Surangsrirat, D., & Intarapanich, A. (2015, April). Analysis of the meditation brainwave from consumer EEG device. IEEE SoutheastCon, Fort Lauderdale.

    Google Scholar 

  98. Lee, N., Broderick, A. J., & Chamberlain, L. (2007). What is “neuromarketing”? A discussion and agenda for future research. International Journal of Psychophysiology, 63(2), 199–204.

    Article  Google Scholar 

  99. Koelstra, S., Muehl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2011). DEAP: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31.

    Article  Google Scholar 

  100. Little, S., Pogosyan, A., Neal, S., Zavala, B., Zrinzo, L., Hariz, M., Foltynie, T., Limousin, P., Ashkan, K., FitzGerald, J., Green, A. L., Aziz, T. Z., & Brown, P. (2013). Adaptive deep brain stimulation in advanced Parkinson disease. Annals of Neurology, 74(3), 449–457.

    Article  Google Scholar 

  101. Stanslaski, S., Afshar, P., Cong, P., Giftakis, J., Stypulkowski, P., Carlson, D., Linde, D., Ullestad, D., Avestruz, A.-T., & Denison, T. (2012). Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 410–421.

    Article  Google Scholar 

  102. Famm, K., Litt, B., Tracey, K. J., Boyden, E. S., & Slaoui, M. (2013). Drug discovery: A jump-start for electroceuticals. Nature, 496(7444), 159–161.

    Article  Google Scholar 

  103. Paulus, W. (2011). Transcranial electrical stimulation (tES–tDCS; tRNS, tACS) methods. Neuropsychological Rehabilitation, 21(5), 602–617.

    Article  Google Scholar 

  104. Kohli, S., & Casson, A. J. (2015). Removal of transcranial ac current stimulation artifact from simultaneous EEG recordings by superposition of moving averages. IEEE Engineering in Medicine and Biology Society, 2015, 3436–3439.

    Google Scholar 

  105. Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123–1134.

    Google Scholar 

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK

    Alexander J. Casson, Mohammed Abdulaal & Siddharth Kohli

  2. School of Materials, The University of Manchester, Manchester, UK

    Meera Dulabh & Eleanor Trimble

  3. EPSRC Centre for Doctoral Training in Sensor Technologies and Applications, The University of Cambridge, Cambridge, UK

    Sammy Krachunov

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  1. Alexander J. Casson
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Correspondence to Alexander J. Casson .

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  1. Waseda University, Shinjuku, Tokyo, Japan

    Toshiyo Tamura

  2. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Wenxi Chen

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Casson, A.J., Abdulaal, M., Dulabh, M., Kohli, S., Krachunov, S., Trimble, E. (2018). Electroencephalogram. In: Tamura, T., Chen, W. (eds) Seamless Healthcare Monitoring. Springer, Cham. https://doi.org/10.1007/978-3-319-69362-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-69362-0_2

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