EEG-Based Brain-Computer Interfaces

  • Yijun WangEmail author
  • Masaki Nakanishi
  • Dan Zhang
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1101)


Brain-computer interfaces (BCIs) provide a direct communication channel between human brain and output devices. Due to advantages such as non-invasiveness, ease of use, and low cost, electroencephalography (EEG) is the most popular method for current BCIs. This chapter gives an overview of the current EEG-based BCIs for the main purpose of communication and control. This chapter first provides a taxonomy of the EEG-based BCI systems by categorizing them into three major groups: (1) BCIs based on event-related potentials (ERPs), (2) BCIs based on sensorimotor rhythms, and (3) hybrid BCIs. Next, this chapter describes challenges and potential solutions in developing practical BCI systems toward high communication speed, convenient system use, and low user variation. Then this chapter briefly reviews both medical and non-medical applications of current BCIs. Finally, this chapter concludes with a summary of current stage and future perspectives of the EEG-based BCI technology.


Brain-computer interfaces EEG-based BCI hybrid BCI ERP SSVEP 



This work is supported by the National Natural Science Foundation of China (61671424, 61335010, and 61634006), the Key project of Chinese Academy of Science (KJZD-EW-L11-01), Beijing S&T planning task (Z161100002616019), and the Recruitment Program of Young Professionals.


  1. 1.
    Vidal JJ (1973) Toward direct brain-computer communication. Annu Rev Riophys Bioeng 2:157–180CrossRefGoogle Scholar
  2. 2.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791PubMedCrossRefGoogle Scholar
  3. 3.
    Birbaumer N (2006) Brain-computer-interface research: coming of age. Clin Neurophysiol 117(3):479–483PubMedCrossRefGoogle Scholar
  4. 4.
    Lebedev MA, Nicolelis MAL (2017) Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol Rev 97(2):767–837PubMedCrossRefGoogle Scholar
  5. 5.
    Vidal JJ (1977) Real-time detection of brain events in EEG. Proc IEEE 65(5):633–664CrossRefGoogle Scholar
  6. 6.
    Farwell LA, Donchin E (1988) Talking off the top of your head – toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523PubMedCrossRefGoogle Scholar
  7. 7.
    Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78(3):252–259PubMedCrossRefGoogle Scholar
  8. 8.
    Pfurtscheller G, Flotzinger D, Kalcher J (1993) Brain-computer interface – a new communication device for handicapped persons. J Microcomput Appl 16(3):293–299CrossRefGoogle Scholar
  9. 9.
    Sutter EE (1992) The brain response interface: communication through visually-induced electrical brain responses. J Microcomput Appl 15(1):31–45CrossRefGoogle Scholar
  10. 10.
    Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralyzed. Nature 398:297–298PubMedCrossRefGoogle Scholar
  11. 11.
    Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 4(2):R32–R57PubMedCrossRefGoogle Scholar
  12. 12.
    Makeig S, Kothe C, Mullen T, Bigdely-Shamlo N, Zhang Z, Kreutz-Delgado K (2012) Evolving signal processing for brain–computer interfaces. Proc IEEE 100:1567–1584CrossRefGoogle Scholar
  13. 13.
    Müller KR, Krauledat M, Dornhege G, Curio G, Blankertz B (2004) Machine learning techniques for brain-computer interfaces. Biomed Tech 49(1):11–22Google Scholar
  14. 14.
    Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 4(2):R1–R13PubMedCrossRefGoogle Scholar
  15. 15.
    Mason SG, Bashashati A, Fatourechi M, Navarro KF, Birch GE (2007) A comprehensive survey of brain interface technology designs. Ann Biomed Eng 35(2):137–169PubMedCrossRefGoogle Scholar
  16. 16.
    Lance BJ, Kerick SE, Ries AJ, Oie KS, McDowell K (2012) Brain computer interface technologies in the coming decades. Proc IEEE 100:1585–1599CrossRefGoogle Scholar
  17. 17.
    Mak JN, Wolpaw JR (2009) Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Van Erp J, Lotte F, Tangermann M (2012) Brain-computer interfaces: beyond medical applications. Computer 45(4):26–34CrossRefGoogle Scholar
  19. 19.
    Gao S, Wang Y, Gao X, Hong B (2014) Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng 61(5):1436–1447PubMedCrossRefGoogle Scholar
  20. 20.
    Müller-Putz G, Leeb R, Tangermann M, Höhne J, Kübler A, Cincotti F, Mattia D, Rupp R, Müller KR, Millan JR (2015) Towards noninvasive hybrid brain–computer interfaces: framework, practice, clinical application, and beyond. Proc IEEE 103(6):926–943CrossRefGoogle Scholar
  21. 21.
    Chaudhary U, Birbaumer N, Ramos-Murguialday A (2016) Brain–computer interfaces for communication and rehabilitation. Nat Rev Neurol 12(9):513–525PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Zander TO, Kothe C (2011) Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J Neural Eng 8(2):025005PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Millán JR, Rupp R, Müller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M, Vidaurre C, Cincotti F, Kübler A, Leeb R, Neuper C, Müller KR, Mattia D (2010) Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci 4:161PubMedPubMedCentralGoogle Scholar
  24. 24.
    Wang Y, Gao X, Hong B, Gao S (2010) Practical designs of brain–computer interfaces based on the modulation of EEG rhythms. In: Graimann B, Allison B, Pfurtscheller G (eds) Brain–computer interfaces. Springer, Heidelberg, pp 137–154Google Scholar
  25. 25.
    Yuan H, He B (2014) Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 61(5):1425–1435PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Luck SJ (2005) An introduction to the event-related potential technique. MIT Press, CambridgeGoogle Scholar
  27. 27.
    Regan D (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New YorkGoogle Scholar
  28. 28.
    Patel SH, Azzam PN (2005) Characterization of N200 and P300: selected studies of the event-related potential. Int J Med Sci 2(4):47–154Google Scholar
  29. 29.
    Rappaport TS (2001) Wireless communication, principle and practice, 2nd edn. Prentice-Hall, Englewood CliffsGoogle Scholar
  30. 30.
    Fazel-Rezai R, Allison BZ, Guger C, Sellers EW, Kleih SC, Kübler A (2012) P300 brain computer interface: current challenges and emerging trends. Front Neuroeng 5:14PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Guo F, Hong B, Gao X, Gao S (2008) A brain–computer interface using motion-onset visual evoked potential. J Neural Eng 5(4):477–485PubMedCrossRefGoogle Scholar
  32. 32.
    Lee PL, Hsieh JC, Wu CH, Shyu KK, Chen SS, Yeh TC, Wu YT (2006) The brain computer interface using flash visual evoked potential and independent component analysis. Ann Biomed Eng 34(10):1641–1654PubMedCrossRefGoogle Scholar
  33. 33.
    Vialatte FB, Maurice M, Dauwels J, Cichocki A (2010) Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol 90(4):418–438PubMedCrossRefGoogle Scholar
  34. 34.
    Chen X, Wang Y, Nakanishi M, Gao X, Jung TP, Gao S (2015) High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A 112(44):E6058–E6067PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Nakanishi M, Wang Y, Chen X, Wang YW, Gao X, Jung TP (2017) Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng. (in press)Google Scholar
  36. 36.
    Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S (2011) A high-speed BCI based on code modulation VEP. J Neural Eng 8(2):025015PubMedCrossRefGoogle Scholar
  37. 37.
    Maye A, Zhang D, Engel AK (2017) Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency. IEEE Trans Neural Syst Rehabil Eng. (in press)Google Scholar
  38. 38.
    Blankertz B, Lemm S, Treder M, Haufe S, Müller KR (2011) Single trial analysis and classification of ERP components—a tutorial. NeuroImage 56(2):814–825PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Parra LC, Spence CD, Gerson AD, Sajda P (2005) Recipes for the linear analysis of EEG. NeuroImage 28(2):326–341PubMedCrossRefPubMedCentralGoogle Scholar
  40. 40.
    Wang Y, Gao X, Hong B, Jia C, Gao S (2008) Brain–computer interfaces based on visual evoked potentials-feasibility of practical system designs. IEEE Eng Med Biol Mag 27(5):64–71PubMedCrossRefGoogle Scholar
  41. 41.
    Riccio A, Mattia D, Simione L, Olivetti M, Cincotti F (2012) Eye-gaze independent EEG-based brain–computer interfaces for communication. J Neural Eng 9(4):045001PubMedCrossRefGoogle Scholar
  42. 42.
    Kelly SP, Lalor EC, Finucane C, McDarby G, Reilly RB (2005) Visual spatial attention control in an independent brain–computer interface. IEEE Trans Biomed Eng 52(9):1588–1596PubMedCrossRefGoogle Scholar
  43. 43.
    Zhang D, Maye A, Gao X, Hong B, Engel AK, Gao S (2010) An independent brain–computer interface using covert non-spatial visual selective attention. J Neural Eng 7(1):016010CrossRefGoogle Scholar
  44. 44.
    Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857PubMedCrossRefGoogle Scholar
  45. 45.
    Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A 101(51):17849–17854PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7(3):036007PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89(7):1123–1134CrossRefGoogle Scholar
  48. 48.
    Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1):153–159PubMedCrossRefGoogle Scholar
  49. 49.
    Wang T, Deng J, He B (2004) Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clin Neurophysiol 115(12):2744–2753PubMedCrossRefGoogle Scholar
  50. 50.
    Li J, Wang Y, Zhang L, Cichocki A, Jung TP (2016) Decoding EEG in cognitive tasks with time-frequency and connectivity masks. IEEE Trans Cogn Dev Syst 8(4):298–308CrossRefGoogle Scholar
  51. 51.
    Ramoser H, Müller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Neural Syst Rehabil Eng 8(4):441–446CrossRefGoogle Scholar
  52. 52.
    Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56CrossRefGoogle Scholar
  53. 53.
    Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Wang Y, Jung TP (2013) Improving brain-computer interfaces using independent component analysis. In: Allison B, Dunne S, Leeb R, Millan JR, Nijholt A (eds) Towards practical brain-computer interfaces: bridging the gap from research to real-world applications. Springer, Heidelberg, pp 67–83Google Scholar
  55. 55.
    Qin L, Ding L, He B (2005) Motor imagery classification by means of source analysis for brain-computer interface applications. J Neural Eng 2(4):65–72PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Edelman BJ, Baxter B, He B (2016) EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Trans Biomed Eng 63(1):4–14PubMedCrossRefGoogle Scholar
  57. 57.
    Wei Q, Wang Y, Gao X, Gao S (2007) Amplitude and phase coupling measures for feature extraction in an EEG-based brain-computer interface. J Neural Eng 4(2):120–129PubMedCrossRefGoogle Scholar
  58. 58.
    Neuper C, Scherer R, Reiner M, Pfurtscheller G (2005) Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res Cogn Brain Res 25(3):668–677PubMedCrossRefGoogle Scholar
  59. 59.
    Vidaurre C, Sannelli C, Müller KR, Blankertz B (2011) Machine-learning-based coadaptive calibration for brain-computer interfaces. Neural Comput 23(3):791–816PubMedCrossRefGoogle Scholar
  60. 60.
    Acqualagna L, Botrel L, Vidaurre C, Kübler A, Blankertz B (2016) Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain computer interface. PLoS ONE 11(2):e0148886PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Kübler A, Neumann N, Wilhelm B, Hinterberger T, Birbaumer N (2004) Predictability of brain-computer communication. J Psychophysiol 18:121–129CrossRefGoogle Scholar
  62. 62.
    Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, Curio G, Dickhaus T (2010) Neurophysiological predictor of SMR-based BCI performance. NeuroImage 51(4):1303–1309PubMedCrossRefGoogle Scholar
  63. 63.
    Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G (2008) The Berlin brain-computer interface: accurate performance from first-session in BCI-naïve subjects. IEEE Trans Biomed Eng 55(10):2452–2462PubMedCrossRefGoogle Scholar
  64. 64.
    Pfurtscheller G, Allison BZ, Bauernfeind G, Brunner C, Solis Escalante T, Scherer R, Zander TO, Müller-Putz G, Neuper C, Birbaumer N (2010) The hybrid BCI. Front Neurosci 4:30PubMedGoogle Scholar
  65. 65.
    Lin K, Cinetto A, Wang Y, Chen X, Gao S, Gao X (2016) An online hybrid BCI system based on SSVEP and EMG. J Neural Eng 13(2):026020PubMedCrossRefGoogle Scholar
  66. 66.
    Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Müller KR, Blankertz B (2012) Enhanced performance by a hybrid NIRS–EEG brain computer interface. NeuroImage 59(1):519–529PubMedCrossRefGoogle Scholar
  67. 67.
    Yin E, Zhou Z, Jiang J, Chen F, Liu Y, Hu D (2013) A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm. J Neural Eng 10(2):026012PubMedCrossRefGoogle Scholar
  68. 68.
    Li Y, Pan J, Long J, Yu T, Wang F, Yu Z, Wu W (2016) Multimodal bcis: target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proc IEEE 104(2):332–352CrossRefGoogle Scholar
  69. 69.
    Xu M, Qi H, Wan B, Yin T, Liu Z, Ming D (2013) A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature. J Neural Eng 10(2):026001PubMedCrossRefGoogle Scholar
  70. 70.
    Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G (2010) Toward a hybrid brain–computer interface based on imagined movement and visualattention. J Neural Eng 7(2):26007PubMedCrossRefGoogle Scholar
  71. 71.
    Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng 57(10):2495–2505PubMedCrossRefGoogle Scholar
  72. 72.
    Höhne J, Holz E, Staiger-Sälzer P, Müller KR, Kübler A, Tangermann M (2014) Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution. PLoS ONE 9(8):e104854PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Kelly SP, Lalor EC, Reilly RB, Foxe JJ (2005) Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans Neural Syst Rehabil Eng 13(2):172–178PubMedCrossRefGoogle Scholar
  74. 74.
    Xu M, Wang Y, Nakanishi M, Wang YT, Qi H, Jung TP, Ming D (2016) Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features. J Neural Eng 13(6):066003PubMedCrossRefGoogle Scholar
  75. 75.
    Billinger M, Daly I, Kaiser V, Jin J, Allison BZ, Müller-Putz GR, Brunner R (2013) Is it significant? Guidelines for reporting BCI performance. In: Allison B, Dunne S, Leeb R, Millan JR, Nijholt A (eds) Towards practical brain-computer interfaces: bridging the gap from research to real-world applications. Springer, Heidelberg, pp 333–354Google Scholar
  76. 76.
    Yuan P, Gao X, Allison B, Wang Y, Bin G, Gao S (2013) A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces. J Neural Eng 10(2):026014PubMedCrossRefGoogle Scholar
  77. 77.
    Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B (2008) Machine learning for real-time single-trial EEG-analysis: from brain – computer interfacing to mental state monitoring. J Neurosci Methods 167(1):82–90PubMedCrossRefGoogle Scholar
  78. 78.
    Townsend G, Platsko V (2016) Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain. J Neural Eng 13(2):026024PubMedCrossRefGoogle Scholar
  79. 79.
    Chen X, Chen Z, Gao S, Gao X (2014) A high-ITR SSVEP-based BCI speller. Brain-Comp Interfaces 1(3–4):181–191CrossRefGoogle Scholar
  80. 80.
    Chen X, Wang Y, Gao S, Jung TP, Gao X (2015) Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng 12(4):046008PubMedCrossRefGoogle Scholar
  81. 81.
    Nakanishi M, Wang Y, Wang YT, Mitsukura Y, Jung TP (2014) A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst 24(6):1450019PubMedCrossRefGoogle Scholar
  82. 82.
    Xu M, Chen L, Zhang L, Qi H, Ma L, Tang J, Wan B, Ming D (2014) A visual parallel-BCI speller based on the time–frequency coding strategy. J Neural Eng 11(2):026014PubMedCrossRefGoogle Scholar
  83. 83.
    Lin Z, Zhang C, Wu W, Gao X (2007) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 54(6):1172–1176PubMedCrossRefGoogle Scholar
  84. 84.
    Wan F, Da Cruz JN, Nan W, Wong CM, Vai MI, Rosa A (2016) Alpha neurofeedback training improves SSVEP-based BCI performance. J Neural Eng 13(3):036019PubMedCrossRefGoogle Scholar
  85. 85.
    Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface. Med Biol Eng Comput 49(2):181–191PubMedCrossRefGoogle Scholar
  86. 86.
    Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M (2013) Optimizing event-related potential based brain–computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng 10(3):036025PubMedCrossRefGoogle Scholar
  87. 87.
    Nakanishi M, Wang Y, Jung TP (2016) Session-to-session transfer in detecting SSVEPs with individual calibration data. In: Schmorrow DD, Fidopiastis CM (eds) Foundations of augmented cognition: neuroergonomics and operational neuroscience. Springer International Publishing, Cham, pp 253–260CrossRefGoogle Scholar
  88. 88.
    Yuan P, Chen X, Wang Y, Gao X, Gao S (2015) Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information. J Neural Eng 12(4):046006PubMedCrossRefGoogle Scholar
  89. 89.
    Xu M, Liu J, Chen L, Qi H, He F, Zhou P, Wan B, Ming D (2016) Incorporation of inter-subject information to improve the accuracy of subject-specific P300 classifiers. Int J Neural Syst 26(3):1650010PubMedCrossRefGoogle Scholar
  90. 90.
    Wang Y, Wang YT, Jung TP (2012) Translation of EEG spatial filters from resting to motor imagery using independent component analysis. PLoS ONE 7(5):e37665PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Vidaurre C, Sannelli C, Müller KR, Blankertz B (2010) Machine-learning-based coadaptive calibration for brain-computer interfaces. Neural Comput 23(3):791–816PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    Cheng M, Gao X, Gao S, Xu D (2002) Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng 49(10):1181–1186PubMedCrossRefPubMedCentralGoogle Scholar
  93. 93.
    Mason SG, Birch GE (2000) A brain-controlled switch for asynchronous control applications. IEEE Trans Biomed Eng 47(10):1297–1307PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Zhang D, Huang B, Wu W, Li S (2015) An idle-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filter. Int J Neural Syst 25(7):1550030PubMedCrossRefPubMedCentralGoogle Scholar
  95. 95.
    Zhang D, Song H, Xu H, Wu W, Gao S, Hong B (2012) An N200 speller integrating the spatial profile for the detection of the non-control state. J Neural Eng 9(2):026016PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    Zhang H, Guan C, Wang C (2008) Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models. IEEE Trans Biomed Eng 55(6):1754–1763PubMedCrossRefPubMedCentralGoogle Scholar
  97. 97.
    Chi YW, Wang YT, Wang Y, Maier C, Jung TP, Cauwenberghs G (2012) Dry and non-contact EEG sensors for mobile brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 20(2):228–235PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    Norton JJ, Lee DS, Lee JW, Lee W, Kwon O, Won P, Jung SY, Cheng H, Jeong JW, Akce A, Umunna S, Na I, Kwon YH, Wang XQ, Liu Z, Paik U, Huang Y, Bretl T, Yeo WH, Rogers JA (2015) Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface. Proc Natl Acad Sci U S A 112(13):3920–3925PubMedPubMedCentralCrossRefGoogle Scholar
  99. 99.
    Wang YT, Nakanishi M, Wang Y, Wei CS, Cheng CK, Jung TP (2017) An online brain-computer interface based on SSVEPs measured from non-hair-bearing areas. IEEE Trans Neural Syst Rehabil Eng 25(1):11–18PubMedCrossRefGoogle Scholar
  100. 100.
    Pacharra M, Debener S, Wascher E (2017) Concealed around-the-ear EEG captures cognitive processing in a visual Simon task. Front Hum Neurosci 11:290PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Looney D, Kidmose P, Park C, Ungstrup M, Rank ML, Rosenkranz K, Mandic DP (2012) The in-the-ear recording concept: use-centered and wearable brain monitoring. IEEE Pulse 3(6):32–42PubMedCrossRefPubMedCentralGoogle Scholar
  102. 102.
    Wang YT, Wang Y, Jung TP (2011) A cell-phone based brain-computer interface for com-munication in daily life. J Neural Eng 8(2):025018PubMedCrossRefPubMedCentralGoogle Scholar
  103. 103.
    Nakanishi M, Wang YT, Jung TP, Zao JK, Chien YY, Diniz-Filho A, Daga FB, Lin YP, Wang Y, Medeiros FA (2017) Detecting glaucoma with a portable brain-computer interface for objective assessment of visual function loss. JAMA Ophthalmol 135(6):550–557PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Gramann K, Gwin JT, Bigdely-Shamlo N, Ferris DP, Makeig S (2010) Visual evoked re-sponses during standing and walking. Front Hum Neurosci 4:202PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Lin YP, Wang Y, Jung TP (2014) Assessing the feasibility of online SSVEP decoding for moving humans using a consumer EEG headset. J Neuroeng Rehabil 11:119PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Sellers EW, Donchin E (2006) A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol 117(3):538–548PubMedCrossRefGoogle Scholar
  107. 107.
    Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Kübler A (2008) A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119(8):1909–1916PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Sellers EW, Vaughan TM, Wolpaw JR (2010) A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler 11(5):449–455PubMedCrossRefPubMedCentralGoogle Scholar
  109. 109.
    Holz EM, Botrel L, Kaufmann T, Kübler A (2015) Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. Arch Phys Med Rehabil 96(3):S16–S26PubMedCrossRefGoogle Scholar
  110. 110.
    Birbaumer N, Piccione F, Silvoni S, Wildgruber M (2012) Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI). Psychol Res 76(2):183–191PubMedCrossRefGoogle Scholar
  111. 111.
    Van Dokkum L, Ward T, Laffont I (2015) Brain computer interfaces for neurorehabilitation-its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 58(1):3–8PubMedCrossRefGoogle Scholar
  112. 112.
    Bos DPO, Reuderink B, van de Laar B, Gürkök H, Mühl C, Poel M, Heylen D (2010) Brain-computer interfacing and games. In: Tan DS, Nijholt A (eds) Brain-computer interfaces. Springer, London, pp 149–178Google Scholar
  113. 113.
    Ahn M, Lee M, Choi J, Jun SC (2014) A review of brain-computer interface games and an opinion survey from researchers, developers and users. Sensors 14(8):14601–14633PubMedCrossRefGoogle Scholar
  114. 114.
    Lal SK, Craig A (2002) Driver fatigue: electroencephalography and psychological assessment. Psychophysiology 39(3):313–321PubMedCrossRefGoogle Scholar
  115. 115.
    Healey J, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst 6(2):156–166CrossRefGoogle Scholar
  116. 116.
    Poulsen AT, Kamronn S, Dmochowski J, Parra LC, Hansen LK (2017) EEG in the classroom: synchronised neural recordings during video presentation. Sci Rep 7:43916PubMedPubMedCentralCrossRefGoogle Scholar
  117. 117.
    Khushaba RN, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40(9):3803–3812CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of SemiconductorsChinese Academy of SciencesBeijingChina
  2. 2.Institute for Neural ComputationUniversity of California San DiegoSan DiegoUSA
  3. 3.Department of PsychologyTsinghua UniversityBeijingChina

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