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Brain-Computer Interface: Advancement and Challenges 원문보기

Sensors, v.21 no.17, 2021년, pp.5746 -   

Mridha, M. F. (Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh) ,  Das, Sujoy Chandra (firoz@bubt.edu.bd (M.F.M.)) ,  Kabir, Muhammad Mohsin (dsujoy.cse@gmail.com (S.C.D.)) ,  Lima, Aklima Akter (mdmkabi@gmail.com (M.M.K.)) ,  Islam, Md. Rashedul (hossain.limuu@gmail.com (A.A.L.)) ,  Watanobe, Yutaka (Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh)

Abstract AI-Helper 아이콘AI-Helper

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that cover...

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참고문헌 (361)

  1. 1. Berger H. Über das elektroenkephalogramm des menschen Archiv. Psychiatr. 1929 87 527 570 10.1007/BF01797193 

  2. 2. Lindsley D.B. Psychological phenomena and the electroencephalogram Electroencephalogr. Clin. Neurophysiol. 1952 4 443 456 10.1016/0013-4694(52)90075-8 12998592 

  3. 3. Vidal J.J. Toward direct brain-computer communication Annu. Rev. Biophys. Bioeng. 1973 2 157 180 10.1146/annurev.bb.02.060173.001105 4583653 

  4. 4. Zeng F.G. Rebscher S. Harrison W. Sun X. Feng H. Cochlear implants: System design, integration, and evaluation IEEE Rev. Biomed. Eng. 2008 1 115 142 10.1109/RBME.2008.2008250 19946565 

  5. 5. Nicolas-Alonso L.F. Gomez-Gil J. Brain computer interfaces: A review Sensors 2012 12 1211 1279 10.3390/s120201211 22438708 

  6. 6. Abiri R. Borhani S. Sellers E.W. Jiang Y. Zhao X. A comprehensive review of EEG-based brain–computer interface paradigms J. Neural Eng. 2019 16 011001 10.1088/1741-2552/aaf12e 30523919 

  7. 7. Tiwari N. Edla D.R. Dodia S. Bablani A. Brain computer interface: A comprehensive survey Biol. Inspired Cogn. Archit. 2018 26 118 129 10.1016/j.bica.2018.10.005 

  8. 8. Vasiljevic G.A.M. de Miranda L.C. Brain–computer interface games based on consumer-grade EEG Devices: A systematic literature review Int. J. Hum. Comput. Interact. 2020 36 105 142 10.1080/10447318.2019.1612213 

  9. 9. Martini M.L. Oermann E.K. Opie N.L. Panov F. Oxley T. Yaeger K. Sensor modalities for brain-computer interface technology: A comprehensive literature review Neurosurgery 2020 86 E108 E117 10.1093/neuros/nyz286 31361011 

  10. 10. Bablani A. Edla D.R. Tripathi D. Cheruku R. Survey on brain-computer interface: An emerging computational intelligence paradigm ACM Comput. Surv. (CSUR) 2019 52 20 10.1145/3297713 

  11. 11. Fleury M. Lioi G. Barillot C. Lécuyer A. A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback Front. Neurosci. 2020 14 528 10.3389/fnins.2020.00528 32655347 

  12. 12. Torres P.E.P. Torres E.A. Hernández-Álvarez M. Yoo S.G. EEG-based BCI emotion recognition: A survey Sensors 2020 20 5083 10.3390/s20185083 32906731 

  13. 13. Zhang X. Yao L. Wang X. Monaghan J.J. Mcalpine D. Zhang Y. A survey on deep learning-based non-invasive brain signals: Recent advances and new frontiers J. Neural Eng. 2021 18 031002 10.1088/1741-2552/abc902 

  14. 14. Gu X. Cao Z. Jolfaei A. Xu P. Wu D. Jung T.P. Lin C.T. EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications IEEE/ACM Trans. Comput. Biol. Bioinform. 2021 10.1109/TCBB.2021.3052811 33465029 

  15. 15. Kitchenham B. Charters S. Guidelines for Performing Systematic Literature Reviews in Software Engineering EBSE Technical Report Keele University and Durham University Joint Report Durham, UK 2007 

  16. 16. Kitchenham B. Procedures for Performing Systematic Reviews Technical Report TR/SE-0401 Keele University Keele, UK 2004 Volume 33 1 26 

  17. 17. Nijholt A. The future of brain-computer interfacing (keynote paper) Proceedings of the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) Dhaka, Bangladesh 13–14 May 2016 156 161 

  18. 18. Padfield N. Zabalza J. Zhao H. Masero V. Ren J. EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges Sensors 2019 19 1423 10.3390/s19061423 30909489 

  19. 19. Hara Y. Brain plasticity and rehabilitation in stroke patients J. Nippon. Med Sch. 2015 82 4 13 10.1272/jnms.82.4 25797869 

  20. 20. Bousseta R. El Ouakouak I. Gharbi M. Regragui F. EEG based brain computer interface for controlling a robot arm movement through thought Irbm 2018 39 129 135 10.1016/j.irbm.2018.02.001 

  21. 21. Perales F.J. Riera L. Ramis S. Guerrero A. Evaluation of a VR system for Pain Management using binaural acoustic stimulation Multimed. Tools Appl. 2019 78 32869 32890 10.1007/s11042-019-07953-y 

  22. 22. Shim M. Hwang H.J. Kim D.W. Lee S.H. Im C.H. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features Schizophr. Res. 2016 176 314 319 10.1016/j.schres.2016.05.007 27427557 

  23. 23. Sharanreddy M. Kulkarni P. Detection of primary brain tumor present in EEG signal using wavelet transform and neural network Int. J. Biol. Med. Res. 2013 4 2855 2859 

  24. 24. Poulos M. Felekis T. Evangelou A. Is it possible to extract a fingerprint for early breast cancer via EEG analysis? Med. Hypotheses 2012 78 711 716 10.1016/j.mehy.2012.02.016 22406095 

  25. 25. Christensen J.A. Koch H. Frandsen R. Kempfner J. Arvastson L. Christensen S.R. Sorensen H.B. Jennum P. Classification of iRBD and Parkinson’s disease patients based on eye movements during sleep Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Osaka, Japan 3–7 July 2013 441 444 

  26. 26. Mikołajewska E. Mikołajewski D. The prospects of brain—Computer interface applications in children Open Med. 2014 9 74 79 10.2478/s11536-013-0249-3 

  27. 27. Mane R. Chouhan T. Guan C. BCI for stroke rehabilitation: Motor and beyond J. Neural Eng. 2020 17 041001 10.1088/1741-2552/aba162 32613947 

  28. 28. Van Dokkum L. Ward T. Laffont I. Brain computer interfaces for neurorehabilitation–its current status as a rehabilitation strategy post-stroke Ann. Phys. Rehabil. Med. 2015 58 3 8 10.1016/j.rehab.2014.09.016 25614021 

  29. 29. Soekadar S.R. Silvoni S. Cohen L.G. Birbaumer N. Brain-machine interfaces in stroke neurorehabilitation Clinical Systems Neuroscience Springer Berlin/Heidelberg, Germany 2015 3 14 

  30. 30. Beudel M. Brown P. Adaptive deep brain stimulation in Parkinson’s disease Park. Relat. Disord. 2016 22 S123 S126 10.1016/j.parkreldis.2015.09.028 

  31. 31. Mohagheghian F. Makkiabadi B. Jalilvand H. Khajehpoor H. Samadzadehaghdam N. Eqlimi E. Deevband M. Computer-aided tinnitus detection based on brain network analysis of EEG functional connectivity J. Biomed. Phys. Eng. 2019 9 687 10.31661/JBPE.V0I0.937 32039100 

  32. 32. Fernández-Caballero A. Navarro E. Fernández-Sotos P. González P. Ricarte J.J. Latorre J.M. Rodriguez-Jimenez R. Human-avatar symbiosis for the treatment of auditory verbal hallucinations in schizophrenia through virtual/augmented reality and brain-computer interfaces Front. Neuroinformatics 2017 11 64 10.3389/fninf.2017.00064 

  33. 33. Dyck M.S. Mathiak K.A. Bergert S. Sarkheil P. Koush Y. Alawi E.M. Zvyagintsev M. Gaebler A.J. Shergill S.S. Mathiak K. Targeting treatment-resistant auditory verbal hallucinations in schizophrenia with fMRI-based neurofeedback–exploring different cases of schizophrenia Front. Psychiatry 2016 7 37 10.3389/fpsyt.2016.00037 27014102 

  34. 34. Ehrlich S. Guan C. Cheng G. A closed-loop brain-computer music interface for continuous affective interaction Proceedings of the 2017 International Conference on Orange Technologies (ICOT) Singapore 8–10 September 2017 176 179 

  35. 35. Placidi G. Cinque L. Di Giamberardino P. Iacoviello D. Spezialetti M. An affective BCI driven by self-induced emotions for people with severe neurological disorders International Conference on Image Analysis and Processing Springer Berlin/Heidelberg, Germany 2017 155 162 

  36. 36. Kerous B. Skola F. Liarokapis F. EEG-based BCI and video games: A progress report Virtual Real. 2018 22 119 135 10.1007/s10055-017-0328-x 

  37. 37. Stein A. Yotam Y. Puzis R. Shani G. Taieb-Maimon M. EEG-triggered dynamic difficulty adjustment for multiplayer games Entertain. Comput. 2018 25 14 25 10.1016/j.entcom.2017.11.003 

  38. 38. Zhang B. Wang J. Fuhlbrigge T. A review of the commercial brain-computer interface technology from perspective of industrial robotics Proceedings of the 2010 IEEE International Conference on Automation and Logistics Hong Kong, China 16–20 August 2010 16–20 8 379 384 

  39. 39. Van De Laar B. Brugman I. Nijboer F. Poel M. Nijholt A. BrainBrush, a multimodal application for creative expressivity Proceedings of the Sixth International Conference on Advances in Computer-Human Interactions (ACHI 2013) Nice, France 24 February–1 March 2013 62 67 

  40. 40. Todd D. McCullagh P.J. Mulvenna M.D. Lightbody G. Investigating the use of brain-computer interaction to facilitate creativity Proceedings of the 3rd Augmented Human International Conference Megève, France 8–9 March 2012 1 8 

  41. 41. Liu Y.T. Wu S.L. Chou K.P. Lin Y.Y. Lu J. Zhang G. Lin W.C. Lin C.T. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Vancouver, BC, Canada 24–29 July 2016 2488 2494 

  42. 42. Binias B. Myszor D. Cyran K.A. A machine learning approach to the detection of pilot’s reaction to unexpected events based on EEG signals Comput. Intell. Neurosci. 2018 2018 2703513 10.1155/2018/2703513 29849544 

  43. 43. Waldert S. Invasive vs. non-invasive neuronal signals for brain-machine interfaces: Will one prevail? Front. Neurosci. 2016 10 295 10.3389/fnins.2016.00295 27445666 

  44. 44. Panoulas K.J. Hadjileontiadis L.J. Panas S.M. Brain-computer interface (BCI): Types, processing perspectives and applications Multimedia Services in Intelligent Environments Springer Berlin/Heidelberg, Germany 2010 299 321 

  45. 45. Wikipedia Contributors Electrocorticography—Wikipedia, The Free Encyclopedia 2021 Available online: https://en.wikipedia.org/w/index.php?title=Electrocorticography&oldid=1032187616 (accessed on 8 July 2021) 

  46. 46. Kuruvilla A. Flink R. Intraoperative electrocorticography in epilepsy surgery: Useful or not? Seizure 2003 12 577 584 10.1016/S1059-1311(03)00095-5 14630497 

  47. 47. Homan R.W. Herman J. Purdy P. Cerebral location of international 10–20 system electrode placement Electroencephalogr. Clin. Neurophysiol. 1987 66 376 382 10.1016/0013-4694(87)90206-9 2435517 

  48. 48. Cohen D. Magnetoencephalography: Evidence of magnetic fields produced by alpha-rhythm currents Science 1968 161 784 786 10.1126/science.161.3843.784 5663803 

  49. 49. Wikipedia Contributors Human Brain—Wikipedia, The Free Encyclopedia 2021 Available online: https://en.wikipedia.org/w/index.php?title=Human_brain&oldid=1032229379 (accessed on 8 July 2021) 

  50. 50. Zimmerman J. Thiene P. Harding J. Design and operation of stable rf-biased superconducting point-contact quantum devices, and a note on the properties of perfectly clean metal contacts J. Appl. Phys. 1970 41 1572 1580 10.1063/1.1659074 

  51. 51. Wilson J.A. Felton E.A. Garell P.C. Schalk G. Williams J.C. ECoG factors underlying multimodal control of a brain-computer interface IEEE Trans. Neural Syst. Rehabil. Eng. 2006 14 246 250 10.1109/TNSRE.2006.875570 16792305 

  52. 52. Weiskopf N. Veit R. Erb M. Mathiak K. Grodd W. Goebel R. Birbaumer N. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data Neuroimage 2003 19 577 586 10.1016/S1053-8119(03)00145-9 12880789 

  53. 53. Ramadan R.A. Vasilakos A.V. Brain computer interface: Control signals review Neurocomputing 2017 223 26 44 10.1016/j.neucom.2016.10.024 

  54. 54. Huisman T. Diffusion-weighted and diffusion tensor imaging of the brain, made easy Cancer Imaging 2010 10 S163 10.1102/1470-7330.2010.9023 20880787 

  55. 55. Borkowski K. Krzyżak A.T. Analysis and correction of errors in DTI-based tractography due to diffusion gradient inhomogeneity J. Magn. Reson. 2018 296 5 11 10.1016/j.jmr.2018.08.011 30195248 

  56. 56. Purnell J. Klopfenstein B. Stevens A. Havel P.J. Adams S. Dunn T. Krisky C. Rooney W. Brain functional magnetic resonance imaging response to glucose and fructose infusions in humans Diabetes Obes. Metab. 2011 13 229 234 10.1111/j.1463-1326.2010.01340.x 21205113 

  57. 57. Tai Y. Piccini P. Applications of positron emission tomography (PET) in neurology J. Neurol. Neurosurg. Psychiatry 2004 75 669 676 10.1136/jnnp.2003.028175 15090557 

  58. 58. Walker S.M. Lim I. Lindenberg L. Mena E. Choyke P.L. Turkbey B. Positron emission tomography (PET) radiotracers for prostate cancer imaging Abdom. Radiol. 2020 45 2165 2175 10.1007/s00261-020-02427-4 

  59. 59. Wang Y. Wang R. Gao X. Hong B. Gao S. A practical VEP-based brain-computer interface IEEE Trans. Neural Syst. Rehabil. Eng. 2006 14 234 240 10.1109/TNSRE.2006.875576 16792302 

  60. 60. Lim J.H. Hwang H.J. Han C.H. Jung K.Y. Im C.H. Classification of binary intentions for individuals with impaired oculomotor function: ‘eyes-closed’ SSVEP-based brain–computer interface (BCI) J. Neural Eng. 2013 10 026021 10.1088/1741-2560/10/2/026021 23528484 

  61. 61. Bera T.K. Noninvasive electromagnetic methods for brain monitoring: A technical review Brain-Computer Interfaces Springer Berlin/Heidelberg, Germany 2015 51 95 

  62. 62. Zhu D. Bieger J. Garcia Molina G. Aarts R.M. A survey of stimulation methods used in SSVEP-based BCIs Comput. Intell. Neurosci. 2010 2010 702357 10.1155/2010/702357 20224799 

  63. 63. Polich J. Updating P300: An integrative theory of P3a and P3b Clin. Neurophysiol. 2007 118 2128 2148 10.1016/j.clinph.2007.04.019 17573239 

  64. 64. Golub M.D. Chase S.M. Batista A.P. Byron M.Y. Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control Curr. Opin. Neurobiol. 2016 37 53 58 10.1016/j.conb.2015.12.005 26796293 

  65. 65. Kim J.H. Kim B.C. Byun Y.T. Jhon Y.M. Lee S. Woo D.H. Kim S.H. All-optical AND gate using cross-gain modulation in semiconductor optical amplifiers Jpn. J. Appl. Phys. 2004 43 608 10.1143/JJAP.43.608 

  66. 66. Dobrea M.C. Dobrea D.M. The selection of proper discriminative cognitive tasks—A necessary prerequisite in high-quality BCI applications Proceedings of the 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies Bratislava, Slovakia 24–27 November 2009 1 6 

  67. 67. Penny W.D. Roberts S.J. Curran E.A. Stokes M.J. EEG-based communication: A pattern recognition approach IEEE Trans. Rehabil. Eng. 2000 8 214 215 10.1109/86.847820 10896191 

  68. 68. Amiri S. Fazel-Rezai R. Asadpour V. A review of hybrid brain-computer interface systems Adv. Hum. Comput. Interact. 2013 2013 187024 10.1155/2013/187024 

  69. 69. Mustafa M. Auditory Evoked Potential (AEP) Based Brain-Computer Interface (BCI) Technology: A Short Review Adv. Robot. Autom. Data Anal. 2021 1350 272 

  70. 70. Cho H. Ahn M. Ahn S. Kwon M. Jun S.C. EEG datasets for motor imagery brain–computer interface GigaScience 2017 6 gix034 10.1093/gigascience/gix034 28472337 

  71. 71. Gaur P. Gupta H. Chowdhury A. McCreadie K. Pachori R.B. Wang H. A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI IEEE Trans. Instrum. Meas. 2021 70 1 9 10.1109/TIM.2021.3051996 33776080 

  72. 72. Long J. Li Y. Yu T. Gu Z. Target selection with hybrid feature for BCI-based 2-D cursor control IEEE Trans. Biomed. Eng. 2011 59 132 140 10.1109/TBME.2011.2167718 21926016 

  73. 73. Ahn S. Ahn M. Cho H. Jun S.C. Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery J. Neural Eng. 2014 11 066004 10.1088/1741-2560/11/6/066004 25307730 

  74. 74. Wang H. Li Y. Long J. Yu T. Gu Z. An asynchronous wheelchair control by hybrid EEG–EOG brain-computer interface Cogn. Neurodyn. 2014 8 399 409 10.1007/s11571-014-9296-y 25206933 

  75. 75. Alomari M.H. AbuBaker A. Turani A. Baniyounes A.M. Manasreh A. EEG mouse: A machine learning-based brain computer interface Int. J. Adv. Comput. Sci. Appl. 2014 5 193 198 

  76. 76. Xu B.G. Song A.G. Pattern recognition of motor imagery EEG using wavelet transform J. Biomed. Sci. Eng. 2008 1 64 10.4236/jbise.2008.11010 

  77. 77. Wang X. Hersche M. Tömekce B. Kaya B. Magno M. Benini L. An accurate eegnet-based motor-imagery brain–computer interface for low-power edge computing Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Bari, Italy 1 June–1 July 2020 1 6 

  78. 78. Kayikcioglu T. Aydemir O. A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data Pattern Recognit. Lett. 2010 31 1207 1215 10.1016/j.patrec.2010.04.009 

  79. 79. Loboda A. Margineanu A. Rotariu G. Lazar A.M. Discrimination of EEG-based motor imagery tasks by means of a simple phase information method Int. J. Adv. Res. Artif. Intell. 2014 3 10 10.14569/IJARAI.2014.031002 

  80. 80. Alexandre B. Rafal C. Grasp-and-Lift EEG Detection, Identify Hand Motions from EEG Recordings Competition Dataset Available online: https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data (accessed on 19 August 2021) 

  81. 81. Chen X. Zhao B. Wang Y. Xu S. Gao X. Control of a 7-DOF robotic arm system with an SSVEP-based BCI Int. J. Neural Syst. 2018 28 1850018 10.1142/S0129065718500181 29768990 

  82. 82. Lin B. Deng S. Gao H. Yin J. A multi-scale activity transition network for data translation in EEG signals decoding IEEE/ACM Trans. Comput. Biol. Bioinform. 2020 10.1109/TCBB.2020.3024228 

  83. 83. Neuper C. Müller-Putz G.R. Scherer R. Pfurtscheller G. Motor imagery and EEG-based control of spelling devices and neuroprostheses Prog. Brain Res. 2006 159 393 409 17071244 

  84. 84. Ko W. Yoon J. Kang E. Jun E. Choi J.S. Suk H.I. Deep recurrent spatio-temporal neural network for motor imagery based BCI Proceedings of the 2018 6th International Conference on Brain-Computer Interface (BCI) Gangwon, Korea 15–17 January 2018 1 3 

  85. 85. Duan F. Lin D. Li W. Zhang Z. Design of a multimodal EEG-based hybrid BCI system with visual servo module IEEE Trans. Auton. Ment. Dev. 2015 7 332 341 10.1109/TAMD.2015.2434951 

  86. 86. Kaya M. Binli M.K. Ozbay E. Yanar H. Mishchenko Y. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces Sci. Data 2018 5 1 16 10.1038/sdata.2018.211 30482902 

  87. 87. Duan L. Zhong H. Miao J. Yang Z. Ma W. Zhang X. A voting optimized strategy based on ELM for improving classification of motor imagery BCI data Cogn. Comput. 2014 6 477 483 10.1007/s12559-014-9264-1 

  88. 88. Hossain I. Khosravi A. Hettiarachchi I. Nahavandi S. Multiclass informative instance transfer learning framework for motor imagery-based brain-computer interface Comput. Intell. Neurosci. 2018 2018 6323414 10.1155/2018/6323414 29681924 

  89. 89. Khan M.A. Das R. Iversen H.K. Puthusserypady S. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application Comput. Biol. Med. 2020 123 103843 10.1016/j.compbiomed.2020.103843 32768038 

  90. 90. Duan L. Bao M. Miao J. Xu Y. Chen J. Classification based on multilayer extreme learning machine for motor imagery task from EEG signals Procedia Comput. Sci. 2016 88 176 184 10.1016/j.procs.2016.07.422 

  91. 91. Velasco-Álvarez F. Ron-Angevin R. da Silva-Sauer L. Sancha-Ros S. Audio-cued motor imagery-based brain–computer interface: Navigation through virtual and real environments Neurocomputing 2013 121 89 98 10.1016/j.neucom.2012.11.038 

  92. 92. Ahn M. Jun S.C. Performance variation in motor imagery brain–computer interface: A brief review J. Neurosci. Methods 2015 243 103 110 10.1016/j.jneumeth.2015.01.033 25668430 

  93. 93. Blankertz B. Müller K.R. Krusienski D. Schalk G. Wolpaw J.R. Schlögl A. Pfurtscheller G. Millán J.d.R. Schröder M. Birbaumer N. BCI Competition iii 2005 Available online: http://www.bbci.de/competition/iii/ (accessed on 19 August 2021) 

  94. 94. Blankertz B. Muller K.R. Krusienski D.J. Schalk G. Wolpaw J.R. Schlogl A. Pfurtscheller G. Millan J.R. Schroder M. Birbaumer N. The BCI competition III: Validating alternative approaches to actual BCI problems IEEE Trans. Neural Syst. Rehabil. Eng. 2006 14 153 159 10.1109/TNSRE.2006.875642 16792282 

  95. 95. Jin J. Miao Y. Daly I. Zuo C. Hu D. Cichocki A. Correlation-based channel selection and regularized feature optimization for MI-based BCI Neural Netw. 2019 118 262 270 10.1016/j.neunet.2019.07.008 31326660 

  96. 96. Lemm S. Schafer C. Curio G. BCI competition 2003-data set III: Probabilistic modeling of sensorimotor/spl mu/rhythms for classification of imaginary hand movements IEEE Trans. Biomed. Eng. 2004 51 1077 1080 10.1109/TBME.2004.827076 15188882 

  97. 97. Tangermann M. Müller K.R. Aertsen A. Birbaumer N. Braun C. Brunner C. Leeb R. Mehring C. Miller K.J. Mueller-Putz G. Review of the BCI competition IV Front. Neurosci. 2012 6 55 22811657 

  98. 98. Park Y. Chung W. Frequency-optimized local region common spatial pattern approach for motor imagery classification IEEE Trans. Neural Syst. Rehabil. Eng. 2019 27 1378 1388 10.1109/TNSRE.2019.2922713 31199263 

  99. 99. Wang D. Miao D. Blohm G. Multi-class motor imagery EEG decoding for brain-computer interfaces Front. Neurosci. 2012 6 151 10.3389/fnins.2012.00151 23087607 

  100. 100. Nguyen T. Hettiarachchi I. Khatami A. Gordon-Brown L. Lim C.P. Nahavandi S. Classification of multi-class BCI data by common spatial pattern and fuzzy system IEEE Access 2018 6 27873 27884 10.1109/ACCESS.2018.2841051 

  101. 101. Satti A. Guan C. Coyle D. Prasad G. A covariate shift minimisation method to alleviate non-stationarity effects for an adaptive brain-computer interface Proceedings of the 2010 20th International Conference on Pattern Recognition Istanbul, Turkey 23–26 August 2010 105 108 

  102. 102. Sakhavi S. Guan C. Yan S. Parallel convolutional-linear neural network for motor imagery classification Proceedings of the 2015 23rd European Signal Processing Conference (EUSIPCO) Nice, France 31 August–4 September 2015 2736 2740 

  103. 103. Raza H. Cecotti H. Li Y. Prasad G. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface Soft Comput. 2016 20 3085 3096 10.1007/s00500-015-1937-5 

  104. 104. Selim S. Tantawi M.M. Shedeed H.A. Badr A. A CSP∖AM-BA-SVM Approach for Motor Imagery BCI System IEEE Access 2018 6 49192 49208 10.1109/ACCESS.2018.2868178 

  105. 105. Hersche M. Rellstab T. Schiavone P.D. Cavigelli L. Benini L. Rahimi A. Fast and accurate multiclass inference for MI-BCIs using large multiscale temporal and spectral features Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO) Rome, Italy 3–7 September 2018 1690 1694 

  106. 106. Sakhavi S. Guan C. Yan S. Learning temporal information for brain-computer interface using convolutional neural networks IEEE Trans. Neural Netw. Learn. Syst. 2018 29 5619 5629 10.1109/TNNLS.2018.2789927 29994075 

  107. 107. Hossain I. Khosravi A. Nahavandhi S. Active transfer learning and selective instance transfer with active learning for motor imagery based BCI Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN) Vancouver, BC, Canada 24–29 July 2016 4048 4055 

  108. 108. Zhu X. Li P. Li C. Yao D. Zhang R. Xu P. Separated channel convolutional neural network to realize the training free motor imagery BCI systems Biomed. Signal Process. Control. 2019 49 396 403 10.1016/j.bspc.2018.12.027 

  109. 109. Sun L. Feng Z. Chen B. Lu N. A contralateral channel guided model for EEG based motor imagery classification Biomed. Signal Process. Control. 2018 41 1 9 10.1016/j.bspc.2017.10.012 

  110. 110. Uran A. Van Gemeren C. van Diepen R. Chavarriaga R. Millán J.d.R. Applying transfer learning to deep learned models for EEG analysis arXiv 2019 1907.01332 

  111. 111. Gandhi V. Prasad G. Coyle D. Behera L. McGinnity T.M. Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques Neurocomputing 2015 170 161 167 10.1016/j.neucom.2014.12.114 

  112. 112. Ha K.W. Jeong J.W. Motor imagery EEG classification using capsule networks Sensors 2019 19 2854 10.3390/s19132854 

  113. 113. Schirrmeister R.T. Springenberg J.T. Fiederer L.D.J. Glasstetter M. Eggensperger K. Tangermann M. Hutter F. Burgard W. Ball T. Deep learning with convolutional neural networks for EEG decoding and visualization Hum. Brain Mapp. 2017 10.1002/hbm.23730 28782865 

  114. 114. Ahn M. Cho H. Ahn S. Jun S.C. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery PLoS ONE 2013 8 e80886 10.1371/journal.pone.0080886 24278339 

  115. 115. Amin S.U. Alsulaiman M. Muhammad G. Mekhtiche M.A. Hossain M.S. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion Future Gener. Comput. Syst. 2019 101 542 554 10.1016/j.future.2019.06.027 

  116. 116. Li Y. Zhang X.R. Zhang B. Lei M.Y. Cui W.G. Guo Y.Z. A channel-projection mixed-scale convolutional neural network for motor imagery EEG decoding IEEE Trans. Neural Syst. Rehabil. Eng. 2019 27 1170 1180 10.1109/TNSRE.2019.2915621 31071048 

  117. 117. Ahn M. Ahn S. Hong J.H. Cho H. Kim K. Kim B.S. Chang J.W. Jun S.C. Gamma band activity associated with BCI performance: Simultaneous MEG/EEG study Front. Hum. Neurosci. 2013 7 848 10.3389/fnhum.2013.00848 24367322 

  118. 118. Wang W. Degenhart A.D. Sudre G.P. Pomerleau D.A. Tyler-Kabara E.C. Decoding semantic information from human electrocorticographic (ECoG) signals Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2011 2011 6294 6298 22255777 

  119. 119. Williams J.J. Rouse A.G. Thongpang S. Williams J.C. Moran D.W. Differentiating closed-loop cortical intention from rest: Building an asynchronous electrocorticographic BCI J. Neural Eng. 2013 10 046001 10.1088/1741-2560/10/4/046001 23715295 

  120. 120. Li Z. Qiu L. Li R. He Z. Xiao J. Liang Y. Wang F. Pan J. Enhancing BCI-Based emotion recognition using an improved particle swarm optimization for feature selection Sensors 2020 20 3028 10.3390/s20113028 

  121. 121. Onose G. Grozea C. Anghelescu A. Daia C. Sinescu C. Ciurea A. Spircu T. Mirea A. Andone I. Spânu A. On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up Spinal Cord 2012 50 599 608 10.1038/sc.2012.14 22410845 

  122. 122. Meng J. Streitz T. Gulachek N. Suma D. He B. Three-dimensional brain–computer interface control through simultaneous overt spatial attentional and motor imagery tasks IEEE Trans. Biomed. Eng. 2018 65 2417 2427 10.1109/TBME.2018.2872855 30281428 

  123. 123. Kosmyna N. Tarpin-Bernard F. Rivet B. Towards brain computer interfaces for recreational activities: Piloting a drone IFIP Conference on Human-Computer Interaction Springer Berlin/Heidelberg, Germany 2015 506 522 

  124. 124. Dua D. Graff C. UCI Machine Learning Repository University of California Irvine, CA, USA 2017 

  125. 125. Sonkin K.M. Stankevich L.A. Khomenko J.G. Nagornova Z.V. Shemyakina N.V. Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand Artif. Intell. Med. 2015 63 107 117 10.1016/j.artmed.2014.12.006 25547267 

  126. 126. Müller-Putz G.R. Pokorny C. Klobassa D.S. Horki P. A single-switch BCI based on passive and imagined movements: Toward restoring communication in minimally conscious patients Int. J. Neural Syst. 2013 23 1250037 10.1142/S0129065712500372 23578052 

  127. 127. Eskandari P. Erfanian A. Improving the performance of brain-computer interface through meditation practicing Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2008 2008 662 665 19162742 

  128. 128. Edelman B.J. Baxter B. He B. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks IEEE Trans. Biomed. Eng. 2015 63 4 14 10.1109/TBME.2015.2467312 26276986 

  129. 129. Lotte F. Jeunet C. Defining and quantifying users’ mental imagery-based BCI skills: A first step J. Neural Eng. 2018 15 046030 10.1088/1741-2552/aac577 29769435 

  130. 130. Jeunet C. N’Kaoua B. Subramanian S. Hachet M. Lotte F. Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns PLoS ONE 2015 10 e0143962 10.1371/journal.pone.0143962 26625261 

  131. 131. Rathee D. Cecotti H. Prasad G. Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks J. Neural Eng. 2017 14 056005 10.1088/1741-2552/aa785c 28597846 

  132. 132. Sadiq M.T. Yu X. Yuan Z. Aziz M.Z. Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index Sensors 2020 20 5283 10.3390/s20185283 32947766 

  133. 133. Lotte F. Jeunet C. Online classification accuracy is a poor metric to study mental imagery-based bci user learning: An experimental demonstration and new metrics Proceedings of the 7th international BCI conference Pacific Grove, CA, USA 21–25 May 2017 

  134. 134. Wierzgała P. Zapała D. Wojcik G.M. Masiak J. Most popular signal processing methods in motor-imagery BCI: A review and meta-analysis Front. Neuroinformatics 2018 12 78 10.3389/fninf.2018.00078 30459588 

  135. 135. Park C. Looney D. ur Rehman N. Ahrabian A. Mandic D.P. Classification of motor imagery BCI using multivariate empirical mode decomposition IEEE Trans. Neural Syst. Rehabil. Eng. 2012 21 10 22 10.1109/TNSRE.2012.2229296 23204288 

  136. 136. Alexandre B. Rafal C. BCI Challenge @ NER 2015, A Spell on You If You Cannot Detect Errors! Available online: https://www.kaggle.com/c/inria-bci-challenge/data (accessed on 19 August 2021) 

  137. 137. Mahmud M. Kaiser M.S. McGinnity T.M. Hussain A. Deep learning in mining biological data Cogn. Comput. 2021 13 1 33 10.1007/s12559-020-09773-x 33425045 

  138. 138. Cruz A. Pires G. Nunes U.J. Double ErrP detection for automatic error correction in an ERP-based BCI speller IEEE Trans. Neural Syst. Rehabil. Eng. 2017 26 26 36 10.1109/TNSRE.2017.2755018 28945598 

  139. 139. Bhattacharyya S. Konar A. Tibarewala D.N. Hayashibe M. A generic transferable EEG decoder for online detection of error potential in target selection Front. Neurosci. 2017 11 226 10.3389/fnins.2017.00226 28512396 

  140. 140. Jrad N. Congedo M. Phlypo R. Rousseau S. Flamary R. Yger F. Rakotomamonjy A. sw-SVM: Sensor weighting support vector machines for EEG-based brain–computer interfaces J. Neural Eng. 2011 8 056004 10.1088/1741-2560/8/5/056004 21817778 

  141. 141. Zeyl T. Yin E. Keightley M. Chau T. Partially supervised P300 speller adaptation for eventual stimulus timing optimization: Target confidence is superior to error-related potential score as an uncertain label J. Neural Eng. 2016 13 026008 10.1088/1741-2560/13/2/026008 26861029 

  142. 142. Wirth C. Dockree P. Harty S. Lacey E. Arvaneh M. Towards error categorisation in BCI: Single-trial EEG classification between different errors J. Neural Eng. 2019 17 016008 10.1088/1741-2552/ab53fe 31683267 

  143. 143. Combaz A. Chumerin N. Manyakov N.V. Robben A. Suykens J.A. Van Hulle M.M. Towards the detection of error-related potentials and its integration in the context of a P300 speller brain–computer interface Neurocomputing 2012 80 73 82 10.1016/j.neucom.2011.09.013 

  144. 144. Zeyl T. Yin E. Keightley M. Chau T. Improving bit rate in an auditory BCI: Exploiting error-related potentials Brain-Comput. Interfaces 2016 3 75 87 10.1080/2326263X.2016.1169723 

  145. 145. Spüler M. Niethammer C. Error-related potentials during continuous feedback: Using EEG to detect errors of different type and severity Front. Hum. Neurosci. 2015 9 155 25859204 

  146. 146. Kreilinger A. Neuper C. Müller-Putz G.R. Error potential detection during continuous movement of an artificial arm controlled by brain–computer interface Med. Biol. Eng. Comput. 2012 50 223 230 10.1007/s11517-011-0858-4 22210463 

  147. 147. Kreilinger A. Hiebel H. Müller-Putz G.R. Single versus multiple events error potential detection in a BCI-controlled car game with continuous and discrete feedback IEEE Trans. Biomed. Eng. 2015 63 519 529 10.1109/TBME.2015.2465866 26259213 

  148. 148. Dias C.L. Sburlea A.I. Müller-Putz G.R. Masked and unmasked error-related potentials during continuous control and feedback J. Neural Eng. 2018 15 036031 10.1088/1741-2552/aab806 29557346 

  149. 149. Koelstra S. Muhl C. Soleymani M. Lee J.S. Yazdani A. Ebrahimi T. Pun T. Nijholt A. Patras I. Deap: A database for emotion analysis; using physiological signals IEEE Trans. Affect. Comput. 2011 3 18 31 10.1109/T-AFFC.2011.15 

  150. 150. Atkinson J. Campos D. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers Expert Syst. Appl. 2016 47 35 41 10.1016/j.eswa.2015.10.049 

  151. 151. Lan Z. Sourina O. Wang L. Scherer R. Müller-Putz G.R. Domain adaptation techniques for EEG-based emotion recognition: A comparative study on two public datasets IEEE Trans. Cogn. Dev. Syst. 2018 11 85 94 10.1109/TCDS.2018.2826840 

  152. 152. Al-Nafjan A. Hosny M. Al-Wabil A. Al-Ohali Y. Classification of human emotions from electroencephalogram (EEG) signal using deep neural network Int. J. Adv. Comput. Sci. Appl 2017 8 419 425 10.14569/IJACSA.2017.080955 

  153. 153. Chen J. Zhang P. Mao Z. Huang Y. Jiang D. Zhang Y. Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks IEEE Access 2019 7 44317 44328 10.1109/ACCESS.2019.2908285 

  154. 154. Sánchez-Reolid R. García A.S. Vicente-Querol M.A. Fernández-Aguilar L. López M.T. Fernández-Caballero A. González P. Artificial neural networks to assess emotional states from brain-computer interface Electronics 2018 7 384 10.3390/electronics7120384 

  155. 155. Yang Y. Wu Q. Fu Y. Chen X. Continuous convolutional neural network with 3d input for eeg-based emotion recognition International Conference on Neural Information Processing Springer Berlin/Heidelberg, Germany 2018 433 443 

  156. 156. Liu J. Wu G. Luo Y. Qiu S. Yang S. Li W. Bi Y. EEG-based emotion classification using a deep neural network and sparse autoencoder Front. Syst. Neurosci. 2020 14 43 10.3389/fnsys.2020.00043 32982703 

  157. 157. Lim W. Sourina O. Wang L. STEW: Simultaneous task EEG workload data set IEEE Trans. Neural Syst. Rehabil. Eng. 2018 26 2106 2114 10.1109/TNSRE.2018.2872924 30281467 

  158. 158. Savran A. Ciftci K. Chanel G. Mota J. Hong Viet L. Sankur B. Akarun L. Caplier A. Rombaut M. Emotion detection in the loop from brain signals and facial images Proceedings of the eNTERFACE 2006 Workshop Dubrovnik, Croatia 17 July–11 August 2006 

  159. 159. Onton J.A. Makeig S. High-frequency broadband modulation of electroencephalographic spectra Front. Hum. Neurosci. 2009 3 61 10.3389/neuro.09.061.2009 20076775 

  160. 160. Data-EEG-25-users-Neuromarketing, Recorded EEG Signals While Viewing Consumer Products on Computer Screen, Indian Institute of Technology, Roorkee, India Available online: https://drive.google.com/file/d/0B2T1rQUvyyWcSGVVaHZBZzRtTms/view?resourcekey=0-wuVvZnp9Ub89GMoErrxSrQ (accessed on 19 August 2021) 

  161. 161. Yadava M. Kumar P. Saini R. Roy P.P. Dogra D.P. Analysis of EEG signals and its application to neuromarketing Multimed. Tools Appl. 2017 76 19087 19111 10.1007/s11042-017-4580-6 

  162. 162. Aldayel M. Ykhlef M. Al-Nafjan A. Deep learning for EEG-based preference classification in neuromarketing Appl. Sci. 2020 10 1525 10.3390/app10041525 

  163. 163. Zheng W. Liu W. Lu Y. Lu B. Cichocki A. EmotionMeter: A Multimodal Framework for Recognizing Human Emotions IEEE Trans. Cybern. 2018 1 13 10.1109/TCYB.2018.2797176 29994384 

  164. 164. Seidler T.G. Plotkin J.B. Seed dispersal and spatial pattern in tropical trees PLoS Biol. 2006 4 e344 10.1371/journal.pbio.0040344 17048988 

  165. 165. Getzin S. Wiegand T. Hubbell S.P. Stochastically driven adult–recruit associations of tree species on Barro Colorado Island Proc. R. Soc. Biol. Sci. 2014 281 20140922 10.1098/rspb.2014.0922 25030984 

  166. 166. Kong X. Kong W. Fan Q. Zhao Q. Cichocki A. Task-independent eeg identification via low-rank matrix decomposition Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Madrid, Spain 3–6 December 2018 412 419 

  167. 167. González J. Ortega J. Damas M. Martín-Smith P. Gan J.Q. A new multi-objective wrapper method for feature selection–Accuracy and stability analysis for BCI Neurocomputing 2019 333 407 418 10.1016/j.neucom.2019.01.017 

  168. 168. Dalling J.W. Brown T.A. Long-term persistence of pioneer species in tropical rain forest soil seed banks Am. Nat. 2009 173 531 535 10.1086/597221 19228112 

  169. 169. Aznan N.K.N. Atapour-Abarghouei A. Bonner S. Connolly J.D. Al Moubayed N. Breckon T.P. Simulating brain signals: Creating synthetic eeg data via neural-based generative models for improved ssvep classification Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN) Budapest, Hungary 14–19 July 2019 1 8 

  170. 170. Zhong P. Wang D. Miao C. EEG-based emotion recognition using regularized graph neural networks IEEE Trans. Affect. Comput. 2020 10.1109/TAFFC.2020.2994159 

  171. 171. Li H. Jin Y.M. Zheng W.L. Lu B.L. Cross-subject emotion recognition using deep adaptation networks International Conference on Neural Information Processing Springer Berlin/Heidelberg, Germany 2018 403 413 

  172. 172. Thejaswini S. Kumar D.K. Nataraj J.L. Analysis of EEG based emotion detection of DEAP and SEED-IV databases using SVM Proceedings of the Second International Conference on Emerging Trends in Science & Technologies For Engineering Systems (ICETSE-2019) Bengaluru, India 17–18 May 2019 

  173. 173. Liu W. Qiu J.L. Zheng W.L. Lu B.L. Multimodal emotion recognition using deep canonical correlation analysis arXiv 2019 1908.05349 

  174. 174. Rim B. Sung N.J. Min S. Hong M. Deep learning in physiological signal data: A survey Sensors 2020 20 969 10.3390/s20040969 

  175. 175. Cimtay Y. Ekmekcioglu E. Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition Sensors 2020 20 2034 10.3390/s20072034 

  176. 176. Zheng W.L. Lu B.L. A multimodal approach to estimating vigilance using EEG and forehead EOG J. Neural Eng. 2017 14 026017 10.1088/1741-2552/aa5a98 28102833 

  177. 177. Ma B.Q. Li H. Zheng W.L. Lu B.L. Reducing the subject variability of eeg signals with adversarial domain generalization International Conference on Neural Information Processing Springer Berlin/Heidelberg, Germany 2019 30 42 

  178. 178. Ko W. Oh K. Jeon E. Suk H.I. VIGNet: A Deep Convolutional Neural Network for EEG-based Driver Vigilance Estimation Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI) Gangwon, Korea 26–28 February 2020 1 3 

  179. 179. Zhang G. Etemad A. RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces arXiv 2020 2008.08633 

  180. 180. Munoz R. Olivares R. Taramasco C. Villarroel R. Soto R. Barcelos T.S. Merino E. Alonso-Sánchez M.F. Using black hole algorithm to improve eeg-based emotion recognition Comput. Intell. Neurosci. 2018 2018 3050214 10.1155/2018/3050214 29991942 

  181. 181. Izquierdo-Reyes J. Ramirez-Mendoza R.A. Bustamante-Bello M.R. Pons-Rovira J.L. Gonzalez-Vargas J.E. Emotion recognition for semi-autonomous vehicles framework Int. J. Interact. Des. Manuf. 2018 12 1447 1454 10.1007/s12008-018-0473-9 

  182. 182. Xu H. Plataniotis K.N. Subject independent affective states classification using EEG signals Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Orlando, FL, USA 14–16 December 2015 1312 1316 

  183. 183. Drouin-Picaro A. Falk T.H. Using deep neural networks for natural saccade classification from electroencephalograms Proceedings of the 2016 IEEE EMBS International Student Conference (ISC) Ottawa, ON, Canada 29–31 May 2016 1 4 

  184. 184. Al-Nafjan A. Hosny M. Al-Ohali Y. Al-Wabil A. Review and classification of emotion recognition based on EEG brain-computer interface system research: A systematic review Appl. Sci. 2017 7 1239 10.3390/app7121239 

  185. 185. Soleymani M. Pantic M. Multimedia implicit tagging using EEG signals Proceedings of the 2013 IEEE International Conference on Multimedia and Expo (ICME) San Jose, CA, USA 15–19 July 2013 1 6 

  186. 186. Soroush M.Z. Maghooli K. Setarehdan S.K. Nasrabadi A.M. A review on EEG signals based emotion recognition Int. Clin. Neurosci. J. 2017 4 118 10.15171/icnj.2017.01 

  187. 187. Faller J. Cummings J. Saproo S. Sajda P. Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task Proc. Natl. Acad. Sci. USA 2019 116 6482 6490 10.1073/pnas.1817207116 30862731 

  188. 188. Gaume A. Dreyfus G. Vialatte F.B. A cognitive brain–computer interface monitoring sustained attentional variations during a continuous task Cogn. Neurodynamics 2019 13 257 269 10.1007/s11571-019-09521-4 

  189. 189. Pattnaik P.K. Sarraf J. Brain Computer Interface issues on hand movement J. King Saud-Univ.-Comput. Inf. Sci. 2018 30 18 24 10.1016/j.jksuci.2016.09.006 

  190. 190. Weiskopf N. Scharnowski F. Veit R. Goebel R. Birbaumer N. Mathiak K. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI) J. Physiol.-Paris 2004 98 357 373 10.1016/j.jphysparis.2005.09.019 16289548 

  191. 191. Cattan G. Rodrigues P.L.C. Congedo M. EEG Alpha Waves Dataset Ph.D. Thesis GIPSA-LAB, University Grenoble-Alpes Saint-Martin-d’Hères, France 2018 

  192. 192. Grégoire C. Rodrigues P. Congedo M. EEG Alpha Waves Dataset Centre pour la Communication Scientifique Directe Grenoble, France 2019 

  193. 193. Tirupattur P. Rawat Y.S. Spampinato C. Shah M. Thoughtviz: Visualizing human thoughts using generative adversarial network Proceedings of the 26th ACM International Conference on Multimedia Seoul, Korea 22–26 October 2018 950 958 

  194. 194. Walker I. Deisenroth M. Faisal A. Deep Convolutional Neural Networks for Brain Computer Interface Using Motor Imagery Ipmerial College of Science, Technology and Medicine Department of Computing London, UK 2015 68 

  195. 195. Spampinato C. Palazzo S. Kavasidis I. Giordano D. Souly N. Shah M. Deep learning human mind for automated visual classification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Honolulu, HI, USA 21–26 July 2017 6809 6817 

  196. 196. Tan C. Sun F. Zhang W. Deep transfer learning for EEG-based brain computer interface Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Calgary, AB, Canada 15–20 April 2018 916 920 

  197. 197. Xu G. Shen X. Chen S. Zong Y. Zhang C. Yue H. Liu M. Chen F. Che W. A deep transfer convolutional neural network framework for EEG signal classification IEEE Access 2019 7 112767 112776 10.1109/ACCESS.2019.2930958 

  198. 198. Fahimi F. Zhang Z. Goh W.B. Lee T.S. Ang K.K. Guan C. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI J. Neural Eng. 2019 16 026007 10.1088/1741-2552/aaf3f6 30524056 

  199. 199. Tang J. Liu Y. Hu D. Zhou Z. Towards BCI-actuated smart wheelchair system Biomed. Eng. Online 2018 17 1 22 10.1186/s12938-018-0545-x 29310661 

  200. 200. Lawhern V.J. Solon A.J. Waytowich N.R. Gordon S.M. Hung C.P. Lance B.J. EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces J. Neural Eng. 2018 15 056013 10.1088/1741-2552/aace8c 29932424 

  201. 201. Bashivan P. Bidelman G.M. Yeasin M. Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity Eur. J. Neurosci. 2014 40 3774 3784 10.1111/ejn.12749 25288492 

  202. 202. Sprague S.A. McBee M.T. Sellers E.W. The effects of working memory on brain–computer interface performance Clin. Neurophysiol. 2016 127 1331 1341 10.1016/j.clinph.2015.10.038 26620822 

  203. 203. Ramsey N.F. Van De Heuvel M.P. Kho K.H. Leijten F.S. Towards human BCI applications based on cognitive brain systems: An investigation of neural signals recorded from the dorsolateral prefrontal cortex IEEE Trans. Neural Syst. Rehabil. Eng. 2006 14 214 217 10.1109/TNSRE.2006.875582 16792297 

  204. 204. Cutrell E. Tan D. BCI for passive input in HCI Proceedings of the CHI Florence, Italy 5–10 April 2008 Volume 8 1 3 

  205. 205. Riccio A. Simione L. Schettini F. Pizzimenti A. Inghilleri M. Olivetti Belardinelli M. Mattia D. Cincotti F. Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis Front. Hum. Neurosci. 2013 7 732 10.3389/fnhum.2013.00732 24282396 

  206. 206. Schabus M.D. Dang-Vu T.T. Heib D.P.J. Boly M. Desseilles M. Vandewalle G. Schmidt C. Albouy G. Darsaud A. Gais S. The fate of incoming stimuli during NREM sleep is determined by spindles and the phase of the slow oscillation Front. Neurol. 2012 3 40 10.3389/fneur.2012.00040 22493589 

  207. 207. Sun Y. Ye N. Xu X. EEG analysis of alcoholics and controls based on feature extraction Proceedings of the 2006 8th International Conference on Signal Processing Guilin, China 16–20 November 2006 Volume 1 

  208. 208. Nguyen P. Tran D. Huang X. Sharma D. A proposed feature extraction method for EEG-based person identification Proceedings of the 2012 International Conference on Artificial Intelligence Las Vegas, NV, USA 16–19 July 2012 826 831 

  209. 209. Kjøbli J. Tyssen R. Vaglum P. Aasland O. Grønvold N.T. Ekeberg O. Personality traits and drinking to cope as predictors of hazardous drinking among medical students J. Stud. Alcohol 2004 65 582 585 10.15288/jsa.2004.65.582 15536766 

  210. 210. Huang X. Altahat S. Tran D. Sharma D. Human identification with electroencephalogram (EEG) signal processing Proceedings of the 2012 International Symposium on Communications and Information Technologies (ISCIT) Gold Coast, QLD, Australia 2—5 October 2012 1021 1026 

  211. 211. Palaniappan R. Raveendran P. Omatu S. VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics IEEE Trans. Neural Netw. 2002 13 486 491 10.1109/72.991435 18244450 

  212. 212. Zhong S. Ghosh J. HMMs and coupled HMMs for multi-channel EEG classification Proceedings of the 2002 International Joint Conference on Neural Networks Honolulu, HI, USA 12–17 May 2002 Volume 2 1154 1159 

  213. 213. Wang H. Li Y. Hu X. Yang Y. Meng Z. Chang K.M. Using EEG to Improve Massive Open Online Courses Feedback Interaction AIED Workshops Springer Berlin/Heidelberg, Germany 2013 

  214. 214. Wang H. Confused Student EEG Brainwave Data, EEG Data from 10 Students Watching MOOC Videos 2018 Available online: https://www.kaggle.com/wanghaohan/confused-eeg/ (accessed on 19 August 2021) 

  215. 215. Fahimirad M. Kotamjani S.S. A review on application of artificial intelligence in teaching and learning in educational contexts Int. J. Learn. Dev. 2018 8 106 118 10.5296/ijld.v8i4.14057 

  216. 216. Kanoga S. Nakanishi M. Mitsukura Y. Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram Neurocomputing 2016 193 20 32 10.1016/j.neucom.2016.01.057 

  217. 217. Abe K. Sato H. Ohi S. Ohyama M. Feature parameters of eye blinks when the sampling rate is changed Proceedings of the TENCON 2014–2014 IEEE Region 10 Conference Bangkok, Thailand 22–25 October 2014 1 6 

  218. 218. Narejo S. Pasero E. Kulsoom F. EEG based eye state classification using deep belief network and stacked autoencoder Int. J. Electr. Comput. Eng. 2016 6 3131 3141 

  219. 219. Reddy T.K. Behera L. Online eye state recognition from EEG data using deep architectures Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Budapest, Hungary 9–12 October 2016 712 717 

  220. 220. Lim C.K.A. Chia W.C. Chin S.W. A mobile driver safety system: Analysis of single-channel EEG on drowsiness detection Proceedings of the 2014 International Conference on Computational Science and Technology (ICCST) Kota Kinabalu, Malaysia 27–28 August 2014 1 5 

  221. 221. Chun J. Bae B. Jo S. BCI based hybrid interface for 3D object control in virtual reality Proceedings of the 2016 4th International Winter Conference on Brain-Computer Interface (BCI) Gangwon, Korea 22–24 February 2016 1 4 

  222. 222. Agarwal M. Sivakumar R. Blink: A fully automated unsupervised algorithm for eye-blink detection in eeg signals Proceedings of the 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Monticello, IL, USA 24–27 September 2019 1113 1121 

  223. 223. Andreev A. Cattan G. Congedo M. Engineering study on the use of Head-Mounted display for Brain-Computer Interface arXiv 2019 1906.12251 

  224. 224. Agarwal M. Sivakumar R. Charge for a whole day: Extending battery life for bci wearables using a lightweight wake-up command Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems Honolulu, HI, USA 25–30 April 2020 1 14 

  225. 225. Rösler O. Suendermann D. A First Step towards Eye State Prediction Using EEG 2013 Available online: https://www.kaggle.com/c/vibcourseml2020/data/ (accessed on 19 August 2021) 

  226. 226. Zhang Y. Xu P. Guo D. Yao D. Prediction of SSVEP-based BCI performance by the resting-state EEG network J. Neural Eng. 2013 10 066017 10.1088/1741-2560/10/6/066017 24280591 

  227. 227. Hamilton C.R. Shahryari S. Rasheed K.M. Eye state prediction from EEG data using boosted rotational forests Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Miami, FL, USA 9–11 December 2015 429 432 

  228. 228. Kim Y. Lee C. Lim C. Computing intelligence approach for an eye state classification with EEG signal in BCI Proceedings of the 2015 International Conference on Software Engineering and Information Technology (SEIT2015) Guilin, China 26–28 June 2016 265 270 

  229. 229. Agarwal M. Publicly Available EEG Datasets 2021 Available online: https://openbci.com/community/publicly-available-eeg-datasets/ (accessed on 19 August 2021) 

  230. 230. Pan J. Li Y. Gu Z. Yu Z. A comparison study of two P300 speller paradigms for brain–computer interface Cogn. Neurodynamics 2013 7 523 529 10.1007/s11571-013-9253-1 24427224 

  231. 231. Vareka L. Bruha P. Moucek R. Event-related potential datasets based on a three-stimulus paradigm GigaScience 2014 3 2047-217X-3-35 10.1186/2047-217X-3-35 25671095 

  232. 232. Gao W. Guan J.A. Gao J. Zhou D. Multi-ganglion ANN based feature learning with application to P300-BCI signal classification Biomed. Signal Process. Control. 2015 18 127 137 10.1016/j.bspc.2014.12.007 

  233. 233. Marathe A.R. Ries A.J. Lawhern V.J. Lance B.J. Touryan J. McDowell K. Cecotti H. The effect of target and non-target similarity on neural classification performance: A boost from confidence Front. Neurosci. 2015 9 270 10.3389/fnins.2015.00270 26347597 

  234. 234. Shin J. Von Lühmann A. Kim D.W. Mehnert J. Hwang H.J. Müller K.R. Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset Sci. Data 2018 5 1 16 10.1038/sdata.2018.3 30482902 

  235. 235. Håkansson B. Reinfeldt S. Eeg-Olofsson M. Östli P. Taghavi H. Adler J. Gabrielsson J. Stenfelt S. Granström G. A novel bone conduction implant (BCI): Engineering aspects and pre-clinical studies Int. J. Audiol. 2010 49 203 215 10.3109/14992020903264462 20105095 

  236. 236. Guger C. Krausz G. Allison B.Z. Edlinger G. Comparison of dry and gel based electrodes for P300 brain–computer interfaces Front. Neurosci. 2012 6 60 10.3389/fnins.2012.00060 22586362 

  237. 237. Shahriari Y. Vaughan T.M. McCane L. Allison B.Z. Wolpaw J.R. Krusienski D.J. An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data J. Neural Eng. 2019 16 056031 10.1088/1741-2552/ab22ea 31108477 

  238. 238. McCane L.M. Sellers E.W. McFarland D.J. Mak J.N. Carmack C.S. Zeitlin D. Wolpaw J.R. Vaughan T.M. Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis Amyotroph. Lateral Scler. Front. Degener. 2014 15 207 215 10.3109/21678421.2013.865750 24555843 

  239. 239. Miller K.J. Schalk G. Hermes D. Ojemann J.G. Rao R.P. Spontaneous decoding of the timing and content of human object perception from cortical surface recordings reveals complementary information in the event-related potential and broadband spectral change PLoS Comput. Biol. 2016 12 e1004660 10.1371/journal.pcbi.1004660 26820899 

  240. 240. Bobrov P. Frolov A. Cantor C. Fedulova I. Bakhnyan M. Zhavoronkov A. Brain-computer interface based on generation of visual images PLoS ONE 2011 6 e20674 10.1371/journal.pone.0020674 21695206 

  241. 241. Cancino S. Saa J.D. Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders. Applications of Machine Learning 2020 Int. Soc. Opt. Photonics 2020 11511 115110J 

  242. 242. Wei Q. Liu Y. Gao X. Wang Y. Yang C. Lu Z. Gong H. A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes IEEE Trans. Neural Syst. Rehabil. Eng. 2018 26 1178 1187 10.1109/TNSRE.2018.2837501 29877842 

  243. 243. Bin G. Gao X. Wang Y. Li Y. Hong B. Gao S. A high-speed BCI based on code modulation VEP J. Neural Eng. 2011 8 025015 10.1088/1741-2560/8/2/025015 21436527 

  244. 244. Gembler F.W. Benda M. Rezeika A. Stawicki P.R. Volosyak I. Asynchronous c-VEP communication tools—Efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers Sci. Rep. 2020 10 17064 10.1038/s41598-020-74143-4 33051500 

  245. 245. Spüler M. Rosenstiel W. Bogdan M. Online adaptation of a c-VEP brain-computer interface (BCI) based on error-related potentials and unsupervised learning PLoS ONE 2012 7 e51077 10.1371/journal.pone.0051077 23236433 

  246. 246. Kapeller C. Hintermüller C. Abu-Alqumsan M. Prückl R. Peer A. Guger C. A BCI using VEP for continuous control of a mobile robot Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Osaka, Japan 3–7 July 2013 5254 5257 

  247. 247. Spüler M. Rosenstiel W. Bogdan M. One Class SVM and Canonical Correlation Analysis increase performance in a c-VEP based Brain-Computer Interface (BCI) ESANN 2012 10.13140/2.1.2186.7526 

  248. 248. Bin G. Gao X. Wang Y. Hong B. Gao S. VEP-based brain-computer interfaces: Time, frequency, and code modulations [Research Frontier] IEEE Comput. Intell. Mag. 2009 4 22 26 10.1109/MCI.2009.934562 

  249. 249. Zhang Y. Yin E. Li F. Zhang Y. Tanaka T. Zhao Q. Cui Y. Xu P. Yao D. Guo D. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI IEEE Trans. Neural Syst. Rehabil. Eng. 2018 26 1314 1323 10.1109/TNSRE.2018.2848222 29985141 

  250. 250. Wang Y. Chen X. Gao X. Gao S. A benchmark dataset for SSVEP-based brain–computer interfaces IEEE Trans. Neural Syst. Rehabil. Eng. 2016 25 1746 1752 10.1109/TNSRE.2016.2627556 27849543 

  251. 251. Podmore J.J. Breckon T.P. Aznan N.K. Connolly J.D. On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications IEEE Trans. Neural Syst. Rehabil. Eng. 2019 27 611 618 10.1109/TNSRE.2019.2904791 30872236 

  252. 252. Zhang Y. Guo D. Xu P. Zhang Y. Yao D. Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index Cogn. Neurodynamics 2016 10 505 511 10.1007/s11571-016-9398-9 

  253. 253. Lee M.H. Kwon O.Y. Kim Y.J. Kim H.K. Lee Y.E. Williamson J. Fazli S. Lee S.W. EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy GigaScience 2019 8 giz002 10.1093/gigascience/giz002 30698704 

  254. 254. Belwafi K. Romain O. Gannouni S. Ghaffari F. Djemal R. Ouni B. An embedded implementation based on adaptive filter bank for brain–computer interface systems J. Neurosci. Methods 2018 305 1 16 10.1016/j.jneumeth.2018.04.013 29738806 

  255. 255. Rivet B. Souloumiac A. Attina V. Gibert G. xDAWN algorithm to enhance evoked potentials: Application to brain–computer interface IEEE Trans. Biomed. Eng. 2009 56 2035 2043 10.1109/TBME.2009.2012869 19174332 

  256. 256. Lahane P. Jagtap J. Inamdar A. Karne N. Dev R. A review of recent trends in EEG based Brain-Computer Interface Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) Chennai, India 21–23 February 2019 1 6 

  257. 257. Deng S. Winter W. Thorpe S. Srinivasan R. EEG Surface Laplacian using realistic head geometry Int. J. Bioelectromagn. 2011 13 173 177 

  258. 258. Shaw L. Routray A. Statistical features extraction for multivariate pattern analysis in meditation EEG using PCA Proceedings of the 2016 IEEE EMBS International Student Conference (ISC) Ottawa, ON, Canada 29–31 May 2016 1 4 

  259. 259. Subasi A. Gursoy M.I. EEG signal classification using PCA, ICA, LDA and support vector machines Expert Syst. Appl. 2010 37 8659 8666 10.1016/j.eswa.2010.06.065 

  260. 260. Jannat N. Sibli S.A. Shuhag M.A.R. Islam M.R. EEG Motor Signal Analysis-Based Enhanced Motor Activity Recognition Using Optimal De-noising Algorithm Proceedings of the International Joint Conference on Computational Intelligence Springer Berlin/Heidelberg, Germany 2020 125 136 

  261. 261. Vahabi Z. Amirfattahi R. Mirzaei A. Enhancing P300 wave of BCI systems via negentropy in adaptive wavelet denoising J. Med. Signals Sensors 2011 1 165 10.4103/2228-7477.95354 

  262. 262. Johnson M.T. Yuan X. Ren Y. Speech signal enhancement through adaptive wavelet thresholding Speech Commun. 2007 49 123 133 10.1016/j.specom.2006.12.002 

  263. 263. Islam M.R. Rahim M.A. Akter H. Kabir R. Shin J. Optimal IMF selection of EMD for sleep disorder diagnosis using EEG signals Proceedings of the 3rd International Conference on Applications in Information Technology Aizu-Wakamatsu, Japan 1–3 November 2018 96 101 

  264. 264. Bashashati A. Fatourechi M. Ward R.K. Birch G.E. A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals J. Neural Eng. 2007 4 R32 10.1088/1741-2560/4/2/R03 17409474 

  265. 265. Aborisade D. Ojo J. Amole A. Durodola A. Comparative analysis of textural features derived from GLCM for ultrasound liver image classification Int. J. Comput. Trends Technol. 2014 11 6 

  266. 266. He B. Yuan H. Meng J. Gao S. Brain-computer interfaces Neural Engineering Springer Berlin/Heidelberg, Germany 2020 131 183 

  267. 267. Phadikar S. Sinha N. Ghosh R. A survey on feature extraction methods for EEG based emotion recognition International Conference on Innovation in Modern Science and Technology Springer Berlin/Heidelberg, Germany 2019 31 45 

  268. 268. Vaid S. Singh P. Kaur C. EEG signal analysis for BCI interface: A review Proceedings of the 2015 5th International Conference on Advanced Computing & Communication Technologies Haryana, India 21–22 February 2015 143 147 

  269. 269. Sur S. Sinha V.K. Event-related potential: An overview Ind. Psychiatry J. 2009 18 70 10.4103/0972-6748.57865 21234168 

  270. 270. Hajcak G. MacNamara A. Olvet D.M. Event-related potentials, emotion, and emotion regulation: An integrative review Dev. Neuropsychol. 2010 35 129 155 10.1080/87565640903526504 20390599 

  271. 271. Changoluisa V. Varona P. De Borja Rodríguez F. A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond IEEE Access 2020 8 111089 111101 10.1109/ACCESS.2020.3000187 

  272. 272. Beres A.M. Time is of the essence: A review of electroencephalography (EEG) and event-related brain potentials (ERPs) in language research Appl. Psychophysiol. Biofeedback 2017 42 247 255 10.1007/s10484-017-9371-3 28698970 

  273. 273. Takahashi K. Remarks on emotion recognition from bio-potential signals Proceedings of the 2nd International conference on Autonomous Robots and Agents Palmerston North, New Zealand 13–15 December 2004 Volume 1 

  274. 274. Wang X.W. Nie D. Lu B.L. EEG-based emotion recognition using frequency domain features and support vector machines International Conference on Neural Information Processing Springer Berlin/Heidelberg, Germany 2011 734 743 

  275. 275. Islam R. Khan S.A. Kim J.M. Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors J. Sensors 2016 2016 7145715 10.1155/2016/7145715 

  276. 276. Hjorth B. EEG analysis based on time domain properties Electroencephalogr. Clin. Neurophysiol. 1970 29 306 310 10.1016/0013-4694(70)90143-4 4195653 

  277. 277. Dagdevir E. Tokmakci M. Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost Biomed. Signal Process. Control. 2021 67 102548 10.1016/j.bspc.2021.102548 

  278. 278. Feng Z. Qian L. Hu H. Sun Y. Functional Connectivity for Motor Imaginary Recognition in Brain-computer Interface Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Toronto, ON, Canada 11–14 October 2020 3678 3682 10.1109/SMC42975.2020.9283075 

  279. 279. Smith J.O. Mathematics of the Discrete Fourier Transform (DFT): With Audio Applications W3K Publishing Stanford, CA, USA 2007 

  280. 280. Durak L. Arikan O. Short-time Fourier transform: Two fundamental properties and an optimal implementation IEEE Trans. Signal Process. 2003 51 1231 1242 10.1109/TSP.2003.810293 

  281. 281. Zabidi A. Mansor W. Lee Y. Fadzal C.C.W. Short-time Fourier Transform analysis of EEG signal generated during imagined writing Proceedings of the 2012 International Conference on System Engineering and Technology (ICSET) Bandung, Indonesia 11–12 September 2012 1 4 

  282. 282. Al-Fahoum A.S. Al-Fraihat A.A. Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains Int. Sch. Res. Not. 2014 2014 730218 10.1155/2014/730218 

  283. 283. Djamal E.C. Abdullah M.Y. Renaldi F. Brain computer interface game controlling using fast fourier transform and learning vector quantization J. Telecommun. Electron. Comput. Eng. 2017 9 71 74 

  284. 284. Conneau A.C. Essid S. Assessment of new spectral features for eeg-based emotion recognition Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Florence, Italy 4–9 May2014 4698 4702 

  285. 285. Petropulu A.P. Higher-Order Spectral Analysis Digital Signal Procesing Handbook 2018 Available online: http://elektroarsenal.net/higher-order-spectral-analysis.html (accessed on 19 August 2021) 

  286. 286. Aggarwal S. Chugh N. Signal processing techniques for motor imagery brain computer interface: A review Array 2019 1 100003 10.1016/j.array.2019.100003 

  287. 287. LaFleur K. Cassady K. Doud A. Shades K. Rogin E. He B. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface J. Neural Eng. 2013 10 046003 10.1088/1741-2560/10/4/046003 23735712 

  288. 288. Mane A.R. Biradar S. Shastri R. Review paper on feature extraction methods for EEG signal analysis Int. J. Emerg. Trend. Eng. Basic Sci. 2015 2 545 552 

  289. 289. Darvishi S. Al-Ani A. Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Lyon, France 22–26 August 2007 3220 3223 

  290. 290. Nivedha R. Brinda M. Vasanth D. Anvitha M. Suma K. EEG based emotion recognition using SVM and PSO Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) Kerala, India 6–7 July 2017 1597 1600 

  291. 291. Fatourechi M. Bashashati A. Ward R.K. Birch G.E. EMG and EOG artifacts in brain computer interface systems: A survey Clin. Neurophysiol. 2007 118 480 494 10.1016/j.clinph.2006.10.019 17169606 

  292. 292. Wu D. King J.T. Chuang C.H. Lin C.T. Jung T.P. Spatial filtering for EEG-based regression problems in brain–computer interface (BCI) IEEE Trans. Fuzzy Syst. 2017 26 771 781 10.1109/TFUZZ.2017.2688423 

  293. 293. Lotte F. Congedo M. Lécuyer A. Lamarche F. Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces J. Neural Eng. 2007 4 R1 10.1088/1741-2560/4/2/R01 17409472 

  294. 294. Lotte F. Bougrain L. Cichocki A. Clerc M. Congedo M. Rakotomamonjy A. Yger F. A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update J. Neural Eng. 2018 15 031005 10.1088/1741-2552/aab2f2 29488902 

  295. 295. Xanthopoulos P. Pardalos P.M. Trafalis T.B. Linear discriminant analysis Robust Data Mining Springer Berlin/Heidelberg, Germany 2013 27 33 

  296. 296. Gokcen I. Peng J. Comparing linear discriminant analysis and support vector machines International Conference on Advances in Information Systems Springer Berlin/Heidelberg, Germany 2002 104 113 

  297. 297. Schuldt C. Laptev I. Caputo B. Recognizing human actions: A local SVM approach Proceedings of the 17th International Conference on Pattern Recognition Cambridge, UK 26 August 2004 Volume 3 32 36 

  298. 298. Sridhar G. Rao P.M. A Neural Network Approach for EEG classification in BCI Int. J. Comput. Sci. Telecommun. 2012 3 44 48 

  299. 299. Kavasidis I. Palazzo S. Spampinato C. Giordano D. Shah M. Brain2image: Converting brain signals into images Proceedings of the 25th ACM international conference on Multimedia Mountain View, CA, USA 23–27 October 2017 1809 1817 

  300. 300. Rumelhart D.E. Hinton G.E. Williams R.J. Learning Internal Representations by Error Propagation Technical Report California Univ. San Diego La Jolla Inst. for Cognitive Science La Jolla, CA, USA 1985 

  301. 301. Werbos P.J. Generalization of backpropagation with application to a recurrent gas market model Neural Netw. 1988 1 339 356 10.1016/0893-6080(88)90007-X 

  302. 302. Obermaier B. Guger C. Neuper C. Pfurtscheller G. Hidden Markov models for online classification of single trial EEG data Pattern Recognit. Lett. 2001 22 1299 1309 10.1016/S0167-8655(01)00075-7 

  303. 303. Graves A. Mohamed A.r. Hinton G. Speech recognition with deep recurrent neural networks Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing Vancouver, BC, Canada 26–31 May 2013 6645 6649 

  304. 304. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain Psychol. Rev. 1958 65 386 10.1037/h0042519 13602029 

  305. 305. Sunny M.S.H. Afroze N. Hossain E. EEG Band Separation Using Multilayer Perceptron for Efficient Feature Extraction and Perfect BCI Paradigm Proceedings of the 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE) Dhaka, Bangladesh 21–22 December 2020 1 6 

  306. 306. Blumberg J. Rickert J. Waldert S. Schulze-Bonhage A. Aertsen A. Mehring C. Adaptive classification for brain computer interfaces IEEE Trans. Biomed. Eng. 2007 54 2536 2539 

  307. 307. Rezaei S. Tavakolian K. Nasrabadi A.M. Setarehdan S.K. Different classification techniques considering brain computer interface applications J. Neural Eng. 2006 3 139 10.1088/1741-2560/3/2/008 16705270 

  308. 308. Chaudhary P. Agrawal R. A comparative study of linear and non-linear classifiers in sensory motor imagery based brain computer interface J. Comput. Theor. Nanosci. 2019 16 5134 5139 10.1166/jctn.2019.8575 

  309. 309. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition Proc. IEEE 1989 77 257 286 10.1109/5.18626 

  310. 310. Lederman D. Tabrikian J. Classification of multichannel EEG patterns using parallel hidden Markov models Med. Biol. Eng. Comput. 2012 50 319 328 10.1007/s11517-012-0871-2 22407476 

  311. 311. Wang M. Abdelfattah S. Moustafa N. Hu J. Deep Gaussian mixture-hidden Markov model for classification of EEG signals IEEE Trans. Emerg. Top. Comput. Intell. 2018 2 278 287 10.1109/TETCI.2018.2829981 

  312. 312. Liu C. Wang H. Lu Z. EEG classification for multiclass motor imagery BCI Proceedings of the 2013 25th Chinese Control and Decision Conference (CCDC) Guiyang, China 25–27 May 2013 4450 4453 

  313. 313. Bablani A. Edla D.R. Dodia S. Classification of EEG data using k-nearest neighbor approach for concealed information test Procedia Comput. Sci. 2018 143 242 249 10.1016/j.procs.2018.10.392 

  314. 314. Roth P.M. Hirzer M. Köstinger M. Beleznai C. Bischof H. Mahalanobis distance learning for person re-identification Person re-identification Springer Berlin/Heidelberg, Germany 2014 247 267 

  315. 315. Mishuhina V. Jiang X. Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI IEEE Signal Process. Lett. 2018 25 783 787 10.1109/LSP.2018.2823683 

  316. 316. Dou J. Yunus A.P. Bui D.T. Merghadi A. Sahana M. Zhu Z. Chen C.W. Han Z. Pham B.T. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan Landslides 2020 17 641 658 10.1007/s10346-019-01286-5 

  317. 317. Wu D. Xu Y. Lu B.L. Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016 IEEE Trans. Cogn. Dev. Syst. 2020 10.1109/TCDS.2020.3007453 

  318. 318. Zhang C. Kim Y.K. Eskandarian A. EEG-inception: An accurate and robust end-to-end neural network for EEG-based motor imagery classification J. Neural Eng. 2021 18 046014 10.1088/1741-2552/abed81 

  319. 319. Zuo C. Jin J. Xu R. Wu L. Liu C. Miao Y. Wang X. Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain–computer interfaces J. Neural Eng. 2021 18 026018 10.1088/1741-2552/abe20f 

  320. 320. Aler R. Galván I.M. Valls J.M. Evolving spatial and frequency selection filters for brain-computer interfaces Proceedings of the IEEE Congress on Evolutionary Computation Barcelona, Spain 18–23 July 2010 1 7 

  321. 321. Mohamed E.A. Yusoff M.Z.B. Selman N.K. Malik A.S. Enhancing EEG signals in brain computer interface using wavelet transform Int. J. Inf. Electron. Eng. 2014 4 234 10.7763/IJIEE.2014.V4.440 

  322. 322. Carrera-Leon O. Ramirez J.M. Alarcon-Aquino V. Baker M. D’Croz-Baron D. Gomez-Gil P. A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction Proceedings of the 2012 Workshop on Engineering Applications Bogota, Colombia 2–4 May 2012 1 6 

  323. 323. Yang J. Yao S. Wang J. Deep fusion feature learning network for MI-EEG classification IEEE Access 2018 6 79050 79059 10.1109/ACCESS.2018.2877452 

  324. 324. Kanoga S. Kanemura A. Asoh H. A Comparative Study of Features and Classifiers in Single-channel EEG-based Motor Imagery BCI Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Anaheim, CA, USA 26–29 November 2018 474 478 

  325. 325. Yanase J. Triantaphyllou E. A systematic survey of computer-aided diagnosis in medicine: Past and present developments Expert Syst. Appl. 2019 138 112821 10.1016/j.eswa.2019.112821 

  326. 326. Shannon C.E. Warren W. The mathematical theory of communication University of illinois Press Champaign, IL, USA 1949 

  327. 327. Volosyak I. Valbuena D. Malechka T. Peuscher J. Gräser A. Brain–computer interface using water-based electrodes J. Neural Eng. 2010 7 066007 10.1088/1741-2560/7/6/066007 21048286 

  328. 328. Wolpaw J.R. Birbaumer N. McFarland D.J. Pfurtscheller G. Vaughan T.M. Brain–computer interfaces for communication and control Clin. Neurophysiol. 2002 113 767 791 10.1016/S1388-2457(02)00057-3 12048038 

  329. 329. Farwell L.A. Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials Electroencephalogr. Clin. Neurophysiol. 1988 70 510 523 10.1016/0013-4694(88)90149-6 2461285 

  330. 330. Schreuder M. Höhne J. Blankertz B. Haufe S. Dickhaus T. Tangermann M. Optimizing event-related potential based brain–computer interfaces: A systematic evaluation of dynamic stopping methods J. Neural Eng. 2013 10 036025 10.1088/1741-2560/10/3/036025 23685458 

  331. 331. Cohen J. A coefficient of agreement for nominal scales Educ. Psychol. Meas. 1960 20 37 46 10.1177/001316446002000104 

  332. 332. Kraemer H.C. Kappa Coefficient Available online: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat00365 (accessed on 19 August 2021) 

  333. 333. Thompson D.E. Quitadamo L.R. Mainardi L. Gao S. Kindermans P.J. Simeral J.D. Fazel-Rezai R. Matteucci M. Falk T.H. Bianchi L. Performance measurement for brain–computer or brain–machine interfaces: A tutorial J. Neural Eng. 2014 11 035001 10.1088/1741-2560/11/3/035001 24838070 

  334. 334. Chestek C.A. Batista A.P. Santhanam G. Byron M.Y. Afshar A. Cunningham J.P. Gilja V. Ryu S.I. Churchland M.M. Shenoy K.V. Single-neuron stability during repeated reaching in macaque premotor cortex J. Neurosci. 2007 27 10742 10750 10.1523/JNEUROSCI.0959-07.2007 17913908 

  335. 335. Simeral J. Kim S.P. Black M. Donoghue J. Hochberg L. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array J. Neural Eng. 2011 8 025027 10.1088/1741-2560/8/2/025027 21436513 

  336. 336. Gilja V. Nuyujukian P. Chestek C.A. Cunningham J.P. Byron M.Y. Fan J.M. Churchland M.M. Kaufman M.T. Kao J.C. Ryu S.I. A high-performance neural prosthesis enabled by control algorithm design Nat. Neurosci. 2012 15 1752 1757 10.1038/nn.3265 23160043 

  337. 337. Ramos Lopez C. Castro Lopez J. Buchely A. Ordoñez Lopez D. Specialized in Quality Control and Control of Mobile Applications Based on the ISO 9241-11 Ergonomic Requirements for Office Work with Visual Display Terminals (VDTS) 2016 Available online: https://revistas.utp.ac.pa/index.php/memoutp/article/view/1473/ (accessed on 19 August 2021) 

  338. 338. Seffah A. Donyaee M. Kline R.B. Padda H.K. Usability measurement and metrics: A consolidated model Softw. Qual. J. 2006 14 159 178 10.1007/s11219-006-7600-8 

  339. 339. Gupta R. Arndt S. Antons J.N. Schleicher R. Möller S. Falk T.H. Neurophysiological experimental facility for Quality of Experience (QoE) assessment Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013) Ghent, Belgium 27–31 May 2013 1300 1305 

  340. 340. Coyne J.T. Baldwin C. Cole A. Sibley C. Roberts D.M. Applying real time physiological measures of cognitive load to improve training International Conference on Foundations of Augmented Cognition Springer Berlin/Heidelberg, Germany 2009 469 478 

  341. 341. Liu Y.H. Wang S.H. Hu M.R. A self-paced P300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA+ SVM-based detector Appl. Sci. 2016 6 142 10.3390/app6050142 

  342. 342. Tayeb Z. Fedjaev J. Ghaboosi N. Richter C. Everding L. Qu X. Wu Y. Cheng G. Conradt J. Validating deep neural networks for online decoding of motor imagery movements from EEG signals Sensors 2019 19 210 10.3390/s19010210 

  343. 343. Barachant A. Bonnet S. Congedo M. Jutten C. Multiclass brain–computer interface classification by Riemannian geometry IEEE Trans. Biomed. Eng. 2011 59 920 928 10.1109/TBME.2011.2172210 22010143 

  344. 344. Zhang X. Li J. Liu Y. Zhang Z. Wang Z. Luo D. Zhou X. Zhu M. Salman W. Hu G. Design of a fatigue detection system for high-speed trains based on driver vigilance using a wireless wearable EEG Sensors 2017 17 486 10.3390/s17030486 

  345. 345. Zhang Y. Wang Y. Zhou G. Jin J. Wang B. Wang X. Cichocki A. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces Expert Syst. Appl. 2018 96 302 310 10.1016/j.eswa.2017.12.015 

  346. 346. Tomita Y. Vialatte F.B. Dreyfus G. Mitsukura Y. Bakardjian H. Cichocki A. Bimodal BCI using simultaneously NIRS and EEG IEEE Trans. Biomed. Eng. 2014 61 1274 1284 10.1109/TBME.2014.2300492 24658251 

  347. 347. Cecotti H. Graser A. Convolutional neural networks for P300 detection with application to brain-computer interfaces IEEE Trans. Pattern Anal. Mach. Intell. 2010 33 433 445 10.1109/TPAMI.2010.125 

  348. 348. Jin Z. Zhou G. Gao D. Zhang Y. EEG classification using sparse Bayesian extreme learning machine for brain–computer interface Neural Comput. Appl. 2020 32 6601 6609 10.1007/s00521-018-3735-3 

  349. 349. Tsui C.S.L. Gan J.Q. Roberts S.J. A self-paced brain–computer interface for controlling a robot simulator: An online event labelling paradigm and an extended Kalman filter based algorithm for online training Med Biol. Eng. Comput. 2009 47 257 265 10.1007/s11517-009-0459-7 19225819 

  350. 350. Van Erp J. Lotte F. Tangermann M. Brain-computer interfaces: Beyond medical applications Computer 2012 45 26 34 10.1109/MC.2012.107 

  351. 351. Gao S. Wang Y. Gao X. Hong B. Visual and auditory brain–computer interfaces IEEE Trans. Biomed. Eng. 2014 61 1436 1447 24759277 

  352. 352. McCane L.M. Heckman S.M. McFarland D.J. Townsend G. Mak J.N. Sellers E.W. Zeitlin D. Tenteromano L.M. Wolpaw J.R. Vaughan T.M. P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls Clin. Neurophysiol. 2015 126 2124 2131 10.1016/j.clinph.2015.01.013 25703940 

  353. 353. Holz E.M. Botrel L. Kaufmann T. Kübler A. 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. 2015 96 S16 S26 10.1016/j.apmr.2014.03.035 25721543 

  354. 354. Mudgal S.K. Sharma S.K. Chaturvedi J. Sharma A. Brain computer interface advancement in neurosciences: Applications and issues Interdiscip. Neurosurg. 2020 20 100694 10.1016/j.inat.2020.100694 

  355. 355. Shen Y.W. Lin Y.P. Challenge for affective brain-computer interfaces: Non-stationary spatio-spectral EEG oscillations of emotional responses Front. Hum. Neurosci. 2019 13 366 10.3389/fnhum.2019.00366 31736727 

  356. 356. Ghare P.S. Paithane A. Human emotion recognition using non linear and non stationary EEG signal Proceedings of the 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) Pune, India 9–10 September 2016 1013 1016 

  357. 357. Miladinović A. Ajčević M. Jarmolowska J. Marusic U. Colussi M. Silveri G. Battaglini P.P. Accardo A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study Comput. Methods Programs Biomed. 2021 198 105808 10.1016/j.cmpb.2020.105808 33157470 

  358. 358. und Softwaretechnik R. Computational challenges for noninvasive brain computer interfaces IEEE Intell. Syst. 2008 23 78 79 

  359. 359. Allison B.Z. Dunne S. Leeb R. Millán J.D.R. Nijholt A. Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications Springer Science & Business Media New York, NY, USA 2012 

  360. 360. Rashid M. Sulaiman N. PP Abdul Majeed A. Musa R.M. Bari B.S. Khatun S. Current status, challenges, and possible solutions of EEG-based brain-computer interface: A comprehensive review Front. Neurorobotics 2020 14 25 10.3389/fnbot.2020.00025 

  361. 361. Jin J. Allison B.Z. Sellers E.W. Brunner C. Horki P. Wang X. Neuper C. Optimized stimulus presentation patterns for an event-related potential EEG-based brain–computer interface Med. Biol. Eng. Comput. 2011 49 181 191 10.1007/s11517-010-0689-8 20890671 

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