Electroencephalogram

  • Alexander J. Casson
  • Mohammed Abdulaal
  • Meera Dulabh
  • Siddharth Kohli
  • Sammy Krachunov
  • Eleanor Trimble
Chapter

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.

Keywords

Electroencephalography Electrodes Wearables Instrumentation Epilepsy Sleep disorders Consumer neuroscience 

References

  1. 1.
    Berger, H. (1929). Uber das eletrenkephalogram des menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527–570.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 3.
    Lopes da Silva, F. (2009). EEG: Origin and measurement. In C. Mulert & L. Lemieux (Eds.), EEG – fMRI (pp. 19–38). Heidelberg: Springer.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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. 6.
    Lal, S. K., & Craig, A. (2002). Driver fatigue: Electroencephalography and psychological assessment. Psychophysiology, 39(3), 313–321.CrossRefGoogle Scholar
  7. 7.
    Curio, G. (2000). Ain’t no rhythm fast enough: EEG bands beyond beta. Journal of Clinical Neurophysiology, 17(4), 339–340.CrossRefGoogle Scholar
  8. 8.
    Binnie, C. D., Rowan, A. J., & Gutter, T. (1982). A manual of electroencephalographic technology. Cambridge: Cambridge University Press.Google Scholar
  9. 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. 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. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 13.
    Lins, O. G., & Picton, T. W. (1995). Auditory steady-state responses to multiple simultaneous stimuli. Electroencephalography and Clinical Neurophysiology, 96(5), 420–432.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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. 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. 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. 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.CrossRefGoogle Scholar
  20. 20.
    Webster, J. G. (1984). Reducing motion artifacts and interference in biopotential recording. IEEE Transactions on Biomedical Engineering, 31(12), 823–826.CrossRefGoogle Scholar
  21. 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. 22.
    Wilson, S. B., & Emerson, R. (2002). Spike detection: A review and comparison of algorithms. Clinical Neurophysiology, 113(12), 1873–1881.CrossRefGoogle Scholar
  23. 23.
    Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. Cambridge, MA: MIT Press.Google Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 30.
    Logesparan, L., Casson, A. J., & Rodriguez-Villegas, E. (2012). Optimal features for online seizure detection. Medical & Biological Engineering & Computing, 50(7), 659–669.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 32.
    Mallat, S. (1999). A wavelet tour of signal processing (2nd ed.). San Diego: Academic.MATHGoogle Scholar
  33. 33.
    Jentzsch, I., & Sommer, W. (2001). Sequence-sensitive subcomponents of P300: Topographical analyses and dipole source localization. Psychophysiology, 38(4), 607–621.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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. 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.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 46.
    Krachunov, S., & Casson, A. J. (2016). 3D printed dry EEG electrodes. Sensors, 16(10), 1635.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.CrossRefGoogle Scholar
  49. 49.
    Casson, A. J. (2016, August). An introduction to next generation EEG electrodes. IEEE EMBC. Orlando: IEEE.Google Scholar
  50. 50.
    Lopez-Gordo, M. A., Sanchez-Morillo, D., & Pelayo Valle, F. (2014). Dry EEG electrodes. Sensors, 14(7), 12847–12870.CrossRefGoogle Scholar
  51. 51.
    Grass Technologies. http://www.grasstechnologies.com/. Accessed Jan 2017.
  52. 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. 53.
    Smith, P. E. M., & Wallace, S. J. (2001). Clinicians’ guide to epilepsy. London: Arnold.Google Scholar
  54. 54.
    Waterhouse, E. (2003). New horizons in ambulatory electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 22(3), 74–80.CrossRefGoogle Scholar
  55. 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. 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.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. 58.
    Emotiv. https://www.emotiv.com/. Accessed Jan 2017.
  59. 59.
    Muse. http://www.choosemuse.com/. Accessed Jan 2017.
  60. 60.
    Neurosky. http://neurosky.com/. Accessed Jan 2017.
  61. 61.
    Rythm. https://rythm.co/. Accessed Jan 2017.
  62. 62.
    Kokoon. https://kokoon.io/. Accessed Jan 2017.
  63. 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.CrossRefGoogle Scholar
  64. 64.
    OpenBCI. http://openbci.com/. Accessed Jan 2017.
  65. 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.CrossRefGoogle Scholar
  66. 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.CrossRefGoogle Scholar
  67. 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.CrossRefGoogle Scholar
  68. 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.CrossRefGoogle Scholar
  69. 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.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar
  71. 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. 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.CrossRefGoogle Scholar
  73. 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.CrossRefGoogle Scholar
  74. 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. 75.
    Neligan, A., & Sander, J. W. (2015). The incidence and prevalence of epilepsy. Available https://www.epilepsysociety.org.uk/. Accessed Jan 2017.
  76. 76.
    Browne, T. R., & Holmes, G. L. (2001). Epilepsy. The New England Journal of Medicine, 344(15), 1145–1151.CrossRefGoogle Scholar
  77. 77.
    Epilepsy society, diagnosing epilepsy. Available https://www.epilepsysociety.org.uk. Accessed Jan 2017.
  78. 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. 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. 80.
    Carney, P. R., Berry, R. B., & Geyer, J. D. (Eds.). (2005). Clinical sleep disorders. Philadelphia: Lippincott Williams and Wilkins.Google Scholar
  81. 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. 82.
    Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211–1279.CrossRefGoogle Scholar
  83. 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.CrossRefGoogle Scholar
  84. 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. 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.CrossRefGoogle Scholar
  86. 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.CrossRefGoogle Scholar
  87. 87.
    Carlson, T., & Millan, J. R. (2013). Brain-controlled wheelchairs: A robotic architecture. IEEE Journal of Robotics and Automation, 20(1), 65–73.CrossRefGoogle Scholar
  88. 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.CrossRefGoogle Scholar
  89. 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.CrossRefGoogle Scholar
  90. 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.CrossRefGoogle Scholar
  91. 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.CrossRefGoogle Scholar
  92. 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. 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. 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.CrossRefGoogle Scholar
  95. 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.CrossRefGoogle Scholar
  96. 96.
    Transparency market research. Available http://www.prweb.com/releases/2013/11/prweb11337791.htm. Accessed Jan 2017.
  97. 97.
    Surangsrirat, D., & Intarapanich, A. (2015, April). Analysis of the meditation brainwave from consumer EEG device. IEEE SoutheastCon, Fort Lauderdale.Google Scholar
  98. 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.CrossRefGoogle Scholar
  99. 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.CrossRefGoogle Scholar
  100. 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.CrossRefGoogle Scholar
  101. 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.CrossRefGoogle Scholar
  102. 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.CrossRefGoogle Scholar
  103. 103.
    Paulus, W. (2011). Transcranial electrical stimulation (tES–tDCS; tRNS, tACS) methods. Neuropsychological Rehabilitation, 21(5), 602–617.CrossRefGoogle Scholar
  104. 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. 105.
    Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123–1134.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Alexander J. Casson
    • 1
  • Mohammed Abdulaal
    • 1
  • Meera Dulabh
    • 2
  • Siddharth Kohli
    • 1
  • Sammy Krachunov
    • 3
  • Eleanor Trimble
    • 2
  1. 1.School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK
  2. 2.School of MaterialsThe University of ManchesterManchesterUK
  3. 3.EPSRC Centre for Doctoral Training in Sensor Technologies and ApplicationsThe University of CambridgeCambridgeUK

Personalised recommendations