Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Babinski Specialist Psychiatric Healthcare Center Outpatient Addiction Treatment 91 229 Lodz Poland

Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq

Department of Computing and Information Systems University of Greenwich London SE10 9LS UK

Department of Cybernetics and Biomedical Engineering VSB Technical University Ostrava FEECS 708 00 Ostrava Poruba Czech Republic

Department of Theoretical Basis of BioMedical Sciences and Medical Informatics Nicolaus Copernicus University Collegium Medicum 85 067 Bydgoszcz Poland

Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology 45 758 Opole Poland

Institute of Philosophy Kazimierz Wielki University 85 092 Bydgoszcz Poland

The Society for the Substitution Treatment of Addiction Medically Assisted Recovery 85 791 Bydgoszcz Poland

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Shortliffe E.H., Barnett G.O. Biomedical Informatics. Springer; Berlin/Heidelberg, Germany: 2006. Biomedical data: Their acquisition, storage, and use; pp. 46–79.

Kübler A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome. Neuroethics. 2019;13:163–180. doi: 10.1007/s12152-019-09409-4. DOI

Kawala-Janik A. Ph.D. Thesis. University of Greenwich; London, UK: 2013. Efficiency Evaluation of External Environments Control Using Bio-Signals.

Wolpaw J., Wolpaw E.W. Brain-Computer Interfaces: Principles and Practice. OUP USA; Oxford, UK: 2012.

Ma T., Li H., Deng L., Yang H., Lv X., Li P., Li F., Zhang R., Liu T., Yao D., et al. The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential. J. Neural Eng. 2017;14:026015. doi: 10.1088/1741-2552/aa5d5f. PubMed DOI

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. doi: 10.1016/j.rehab.2014.09.016. PubMed DOI

Castro N., Siew C.S. Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process. Proc. R. Soc. A. 2020;476:20190825. doi: 10.1098/rspa.2019.0825. PubMed DOI PMC

Wang Y., Kwong S., Leung H., Lu J., Smith M.H., Trajkovic L., Tunstel E., Plataniotis K.N., Yen G.G., Kinsner W. Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence. IEEE Syst. Man Cybern. Mag. 2020;6:6–13. doi: 10.1109/MSMC.2018.2889502. DOI

Schirmann F. “The wondrous eyes of a new technology”—A history of the early electroencephalography (EEG) of psychopathy, delinquency, and immorality. Front. Hum. Neurosci. 2014;8:232. doi: 10.3389/fnhum.2014.00232. PubMed DOI PMC

Martins N.R., Angelica A., Chakravarthy K., Svidinenko Y., Boehm F.J., Opris I., Lebedev M.A., Swan M., Garan S.A., Rosenfeld J.V., et al. Human brain/cloud interface. Front. Neurosci. 2019;13:112. doi: 10.3389/fnins.2019.00112. PubMed DOI PMC

Collinger J.L., Wodlinger B., Downey J.E., Wang W., Tyler-Kabara E.C., Weber D.J., McMorland A.J., Velliste M., Boninger M.L., Schwartz A.B. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet. 2013;381:557–564. doi: 10.1016/S0140-6736(12)61816-9. PubMed DOI PMC

Theis F.J., Meyer-Bäse A. Biomedical Signal Analysis: Contemporary Methods and Applications. MIT Press; Cambridge, MA, USA: 2010.

Kawala-Sterniuk A., Podpora M., Pelc M., Blaszczyszyn M., Gorzelanczyk E.J., Martinek R., Ozana S. Comparison of smoothing filters in analysis of EEG data for the medical diagnostics purposes. Sensors. 2020;20:807. doi: 10.3390/s20030807. PubMed DOI PMC

Milanizadeh S., Safaie J. EOG Based HCI System for Quadcopter Navigation. IEEE Trans. Instrum. Meas. 2020;69:8992–8999. doi: 10.1109/TIM.2020.3001411. DOI

Saravanakumar D., Reddy R. A high performance asynchronous EOG speller system. Biomed. Signal Process. Control. 2020;59:101898.

Li K., Zhang J., Wang L., Zhang M., Li J., Bao S. A review of the key technologies for sEMG-based human-robot interaction systems. Biomed. Signal Process. Control. 2020;62:102074. doi: 10.1016/j.bspc.2020.102074. DOI

Yao D., Qin Y., Hu S., Dong L., Vega M.L.B., Sosa P.A.V. Which reference should we use for EEG and ERP practice? Brain Topogr. 2019;32:530–549. doi: 10.1007/s10548-019-00707-x. PubMed DOI PMC

Bamdad M., Zarshenas H., Auais M.A. Application of BCI systems in neurorehabilitation: A scoping review. Disabil. Rehabil. Assist. Technol. 2015;10:355–364. doi: 10.3109/17483107.2014.961569. PubMed DOI

Epstein R. The empty brain. Aeon. 2016;18:2016.

Hassan M.A., Rizvi Q.M. Computer vs human brain: An analytical approach and overview. Computer. 2019;6:580–583.

Collinger J.L., Kryger M.A., Barbara R., Betler T., Bowsher K., Brown E.H., Clanton S.T., Degenhart A.D., Foldes S.T., Gaunt R.A., et al. Collaborative approach in the development of High-Performance Brain–Computer interfaces for a neuroprosthetic arm: Translation from animal models to human control. Clin. Transl. Sci. 2014;7:52–59. doi: 10.1111/cts.12086. PubMed DOI PMC

Miller K.J., Hermes D., Staff N.P. The current state of electrocorticography-based brain–computer interfaces. Neurosurg. Focus. 2020;49:E2. doi: 10.3171/2020.4.FOCUS20185. PubMed DOI

Shih J.J., Krusienski D.J., Wolpaw J.R. Mayo Clinic Proceedings. Volume 87. Elsevier; Amsterdam, The Netherlands: 2012. Brain-computer interfaces in medicine; pp. 268–279. PubMed PMC

Zhang H., Wang H. Study on classification and recognition of multi-lead EEG signals. Comput. Eng. Appl. 2008;24:228–230.

Yu X., Qi W. A user study of wearable EEG headset products for emotion analysis; Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence; Sanya, China. 21 December 2018; pp. 1–7.

Adeli H., Zhou Z., Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods. 2003;123:69–87. doi: 10.1016/S0165-0270(02)00340-0. PubMed DOI

Leuthardt E.C., Miller K.J., Schalk G., Rao R.P., Ojemann J.G. Electrocorticography-based brain computer interface-the Seattle experience. IEEE Trans. Neural Syst. Rehabil. Eng. 2006;14:194–198. doi: 10.1109/TNSRE.2006.875536. PubMed DOI

Dubey A., Ray S. Cortical Electrocorticogram (ECoG) is a local signal. J. Neurosci. 2019;39:4299–4311. doi: 10.1523/JNEUROSCI.2917-18.2019. PubMed DOI PMC

Wang P.T., King C.E., McCrimmon C.M., Lin J.J., Sazgar M., Hsu F.P., Shaw S.J., Millet D.E., Chui L.A., Liu C.Y., et al. Comparison of decoding resolution of standard and high-density electrocorticogram electrodes. J. Neural Eng. 2016;13:026016. doi: 10.1088/1741-2560/13/2/026016. PubMed DOI PMC

Chakrabarti S., Sandberg H.M., Brumberg J.S., Krusienski D.J. Progress in speech decoding from the electrocorticogram. Biomed. Eng. Lett. 2015;5:10–21. doi: 10.1007/s13534-015-0175-1. DOI

Graimann B., Allison B.Z., Pfurtscheller G. Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. Springer Science & Business Media; Berlin/Heidelberg, Germany: 2010.

Villamar M.F., Al-Bakri A.F., Haddix C., Albuja A.C., Bensalem-Owen M., Sunderam S. T157. Seizure prediction with autonomic measurements versus intracranial EEG in patients with refractory epilepsy. Clin. Neurophysiol. 2018;129:e63. doi: 10.1016/j.clinph.2018.04.158. PubMed DOI

Wittevrongel B., Khachatryan E., Carrette E., Boon P., Meurs A., Van Roost D., Van Hulle M.M. High-gamma oscillations precede visual steady-state responses: A human electrocorticography study. Hum. Brain Mapp. 2020;41:5341–5355. doi: 10.1002/hbm.25196. PubMed DOI PMC

Amaral P., Paulo J., Cunha S., Dias P., Maria J. Multimodal Application for Visualization and Manipulation of Electrocorticography Data. 2007.

Kingwell K. Neurally controlled robotic arm enables tetraplegic patient to drink coffee of her own volition. Nat. Rev. Neurol. 2012;8:353. doi: 10.1038/nrneurol.2012.101. PubMed DOI

Ethier C., Oby E.R., Bauman M.J., Miller L.E. Restoration of grasp following paralysis through brain-controlled stimulation of muscles. Nature. 2012;485:368–371. doi: 10.1038/nature10987. PubMed DOI PMC

Millett D. Hans Berger: From psychic energy to the EEG. Perspect. Biol. Med. 2001;44:522–542. doi: 10.1353/pbm.2001.0070. PubMed DOI

Gloor P. Hans Berger on electroencephalography. Am. J. EEG Technol. 1969;9:1–8. doi: 10.1080/00029238.1969.11080728. DOI

Berger H. Über das Elektrenkephalogramm des Menschen. XIV. Archiv für Psychiatrie und Nervenkrankheiten. 1938;108:407–431. doi: 10.1007/BF01824101. DOI

Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 2009;36:2027–2036. doi: 10.1016/j.eswa.2007.12.065. DOI

Lopez-Gordo M.A., Sanchez-Morillo D., Valle F.P. Dry EEG electrodes. Sensors. 2014;14:12847–12870. doi: 10.3390/s140712847. PubMed DOI PMC

Beatty J., Greenberg A., Deibler W.P., O’Hanlon J.F. Operant control of occipital theta rhythm affects performance in a radar monitoring task. Science. 1974;183:871–873. doi: 10.1126/science.183.4127.871. PubMed DOI

Tudor M., Tudor L., Tudor K.I. Hans Berger (1873–1941)–the history of electroencephalography. Acta Medica Croat. Cas. Hravatske Akad. Med. Znan. 2005;59:307–313. PubMed

Haas L.F. Hans berger (1873–1941), richard caton (1842–1926), and electroencephalography. J. Neurol. Neurosurg. Psychiatry. 2003;74:9. doi: 10.1136/jnnp.74.1.9. PubMed DOI PMC

Coenen A., Zayachkivska O. Adolf Beck: A pioneer in electroencephalography in between Richard Caton and Hans Berger. Adv. Cogn. Psychol. 2013;9:216. doi: 10.5709/acp-0148-3. PubMed DOI PMC

Kułak W., Sobaniec W. Historia odkrycia EEG. Neurol. Dziecięca. 2006;15:53–56.

Marshall L.H., Magoun H.W. Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function. Springer Science & Business Media; Berlin/Heidelberg, Germany: 2013.

Babkin B. Sechenov and Pavlov. Russ. Rev. 1946;5:24–35. doi: 10.2307/125155. DOI

Grigoriev A., Grigorian N. IM Sechenov: The patriarch of Russian physiology. J. Hist. Neurosci. 2007;16:19–29. doi: 10.1080/09647040600653121. PubMed DOI

Stone J.L., Hughes J.R. Early history of electroencephalography and establishment of the American Clinical Neurophysiology Society. J. Clin. Neurophysiol. 2013;30:28–44. doi: 10.1097/WNP.0b013e31827edb2d. PubMed DOI

Ebersole J.S., Pedley T.A. Current Practice of Clinical Electroencephalography. Lippincott Williams & Wilkins; Philadelphia, PA, USA: 2003.

Hazarika N., Chen J.Z., Tsoi A.C., Sergejew A. Classification of EEG signals using the wavelet transform. Signal Process. 1997;59:61–72. doi: 10.1016/S0165-1684(97)00038-8. DOI

Aydemir E., Tuncer T., Dogan S. A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Med. Hypotheses. 2020;134:109519. doi: 10.1016/j.mehy.2019.109519. PubMed DOI

Shahriari Y., Besio W., Hosni S.I., Zisk A.H., Borgheai S.B., Deligani R.J., McLinden J. Neural Interface Engineering. Springer; Berlin/Heidelberg, Germany: 2020. Electroencephalography; pp. 1–16.

Wojcik G.M., Masiak J., Kawiak A., Schneider P., Kwasniewicz L., Polak N., Gajos-Balinska A. New protocol for quantitative analysis of brain cortex electroencephalographic activity in patients with psychiatric disorders. Front. Neuroinform. 2018;12:27. doi: 10.3389/fninf.2018.00027. PubMed DOI PMC

Ursuţiu D., Samoilă C., Drăgulin S., Constantin F.A. Online Engineering & Internet of Things. Springer; Berlin/Heidelberg, Germany: 2018. Investigation of music and colours influences on the levels of emotion and concentration; pp. 910–918.

Robin M. A Handbook for Yogasana Teachers: The Incorporation of Neuroscience, Physiology, and Anatomy Into the Practice. Wheatmark, Inc.; Tucson, Arizona: 2009.

Akin M. Comparison of wavelet transform and FFT methods in the analysis of EEG signals. J. Med. Syst. 2002;26:241–247. doi: 10.1023/A:1015075101937. PubMed DOI

Jurcak V., Tsuzuki D., Dan I. 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems. Neuroimage. 2007;34:1600–1611. doi: 10.1016/j.neuroimage.2006.09.024. PubMed DOI

Rangayyan R.M. Biomedical Signal Analysis. Volume 33 John Wiley & Sons; Hoboken, NJ, USA: 2015.

Merletti R., Parker P.J. Electromyography: Physiology, Engineering, and Non-Invasive Applications. Volume 11 John Wiley & Sons; Hoboken, NJ, USA: 2004.

Fajkus M., Nedoma J., Martinek R., Vasinek V., Nazeran H., Siska P. A non-invasive multichannel hybrid fiber-optic sensor system for vital sign monitoring. Sensors. 2017;17:111. doi: 10.3390/s17010111. PubMed DOI PMC

Sidikova M., Martinek R., Kawala-Sterniuk A., Ladrova M., Jaros R., Danys L., Simonik P. Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review. Sensors. 2020;20:5699. doi: 10.3390/s20195699. PubMed DOI PMC

Clerc M., Bougrain L., Lotte F. Brain-Computer Interfaces. John Wiley & Sons; Hoboken, NJ, USA: 2016.

Weisz N., Schandry R., Jacobs A.M., Mialet J.P., Duschek S. Early contingent negative variation of the EEG and attentional flexibility are reduced in hypotension. Int. J. Psychophysiol. 2002;45:253–260. doi: 10.1016/S0167-8760(02)00032-6. PubMed DOI

Walter W.G., Cooper R., Aldridge V., McCallum W., Winter A. Contingent negative variation: An electric sign of sensori-motor association and expectancy in the human brain. Nature. 1964;203:380–384. doi: 10.1038/203380a0. PubMed DOI

Sterman M.B., Howe R.C., Macdonald L.R. Facilitation of spindle-burst sleep by conditioning of electroencephalographic activity while awake. Science. 1970;167:1146–1148. doi: 10.1126/science.167.3921.1146. PubMed DOI

Kuhlman W.N. Functional topography of the human mu rhythm. Electroencephalogr. Clin. Neurophysiol. 1978;44:83–93. doi: 10.1016/0013-4694(78)90107-4. PubMed DOI

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. doi: 10.1016/0013-4694(88)90149-6. PubMed DOI

Nguyen T., Hettiarachchi I., Khosravi A., Salaken S.M., Bhatti A., Nahavandi S. Multiclass EEG data classification using fuzzy systems; Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); Naples, Italy. 9–12 July 2017; pp. 1–6.

Arafat I. Brain-Computer Interface: Past, Present & Future. International Islamic University Chittagong (IIUC); Chittagong, Bangladesh: 2013.

Kolhe S., Khemani D., Bhatt C., Dubey N. Emerging Technologies for Health and Medicine: Virtual Reality, Augmented Reality, Artificial Intelligence, Internet of Things, Robotics, Industry 4.0. John Wiley & Sons; Beverly, MA, USA: 2018. Automation of appliances using electro-encephalography; p. 225.

Birbaumer N., Ghanayim N., Hinterberger T., Iversen I., Kotchoubey B., Kübler A., Perelmouter J., Taub E., Flor H. A spelling device for the paralysed. Nature. 1999;398:297–298. doi: 10.1038/18581. PubMed DOI

Rezeika A., Benda M., Stawicki P., Gembler F., Saboor A., Volosyak I. Brain–computer interface spellers: A review. Brain Sci. 2018;8:57. doi: 10.3390/brainsci8040057. PubMed DOI PMC

Hochberg L.R., Bacher D., Jarosiewicz B., Masse N.Y., Simeral J.D., Vogel J., Haddadin S., Liu J., Cash S.S., Van Der Smagt P., et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012;485:372–375. doi: 10.1038/nature11076. PubMed DOI PMC

Gollahalli A.R. Ph.D. Thesis. Auckland University of Technology; Auckland, New Zealand: 2015. Brain-Computer Interfaces for Virtual Quadcopters Based on a Spiking-Neural Network Architecture-Neucube.

Bouton C.E., Shaikhouni A., Annetta N.V., Bockbrader M.A., Friedenberg D.A., Nielson D.M., Sharma G., Sederberg P.B., Glenn B.C., Mysiw W.J., et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature. 2016;533:247–250. doi: 10.1038/nature17435. PubMed DOI

Ganzer P.D., Colachis S.C., 4th, Schwemmer M.A., Friedenberg D.A., Dunlap C.F., Swiftney C.E., Jacobowitz A.F., Weber D.J., Bockbrader M.A., Sharma G. Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface. Cell. 2020;181:763–773.e12. doi: 10.1016/j.cell.2020.03.054. PubMed DOI

Ajiboye A.B., Willett F.R., Young D.R., Memberg W.D., Murphy B.A., Miller J.P., Walter B.L., Sweet J.A., Hoyen H.A., Keith M.W., et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. Lancet. 2017;389:1821–1830. doi: 10.1016/S0140-6736(17)30601-3. PubMed DOI PMC

Willett F.R., Young D.R., Murphy B.A., Memberg W.D., Blabe C.H., Pandarinath C., Stavisky S.D., Rezaii P., Saab J., Walter B.L., et al. Principled BCI decoder design and parameter selection using a feedback control model. Sci. Rep. 2019;9:1–17. doi: 10.1038/s41598-019-44166-7. PubMed DOI PMC

Schwemmer M.A., Skomrock N.D., Sederberg P.B., Ting J.E., Sharma G., Bockbrader M.A., Friedenberg D.A. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework. Nat. Med. 2018;24:1669–1676. doi: 10.1038/s41591-018-0171-y. PubMed DOI

Sitaram R., Caria A., Veit R., Gaber T., Rota G., Kuebler A., Birbaumer N. FMRI brain-computer interface: A tool for neuroscientific research and treatment. Comput. Intell. Neurosci. 2007;2007:025487. doi: 10.1155/2007/25487. PubMed DOI PMC

Birbaumer N. Breaking the silence: Brain–computer interfaces (BCI) for communication and motor control. Psychophysiology. 2006;43:517–532. doi: 10.1111/j.1469-8986.2006.00456.x. PubMed DOI

Kim J., Lee J., Han C., Park K. An Instant Donning Multi-Channel EEG Headset (with Comb-Shaped Dry Electrodes) and BCI Applications. Sensors. 2019;19:1537. doi: 10.3390/s19071537. PubMed DOI PMC

Velasco-Álvarez F., Sancha-Ros S., García-Garaluz E., Fernández-Rodríguez Á., Medina-Juliá M.T., Ron-Angevin R. UMA-BCI speller: An easily configurable P300 speller tool for end users. Comput. Methods Programs Biomed. 2019;172:127–138. doi: 10.1016/j.cmpb.2019.02.015. PubMed DOI

Mowla M.R., Gonzalez-Morales J.D., Rico-Martinez J., Ulichnie D.A., Thompson D.E. A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation. Brain Sci. 2020;10:734. doi: 10.3390/brainsci10100734. PubMed DOI PMC

Al-Saegh A., Dawwd S.A., Abdul-Jabbar J.M. Deep learning for motor imagery EEG-based classification: A review. Biomed. Signal Process. Control. 2021;63:102172. doi: 10.1016/j.bspc.2020.102172. DOI

Yoo S.S., Fairneny T., Chen N.K., Choo S.E., Panych L.P., Park H., Lee S.Y., Jolesz F.A. Brain—Computer interface using fMRI: Spatial navigation by thoughts. Neuroreport. 2004;15:1591–1595. doi: 10.1097/01.wnr.0000133296.39160.fe. PubMed DOI

Montagna F. Ph.D. Thesis. University of Bologna; Bologna, Italy: 2020. Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures.

Maymandi H., Perez-Benitez J., Gallegos-Funesa F., Perez-Benitez J. A Novel Monitor for Practical Brain-Computer Interface Applications Based on Visual Evoked Potential. 2020. in preprint.

Hasan M.A., Khan M.U., Mishra D. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. BioMed Res. Int. 2020;2020:1838140. doi: 10.1155/2020/1838140. PubMed DOI PMC

Wolpaw J., McFarland D. Development of an EEG-based brain-computer interface (BCI) Rehabil. Eng. Soc. N. Am. 1995;15:645–648.

Flotzinger D., Kalcher J., Wolpaw J. Off-Line Classification of EEG from the “New York Brain-Computer Interface (BCI)”. Technische Universität Graz/Österreichische Computer Gesellschaft; Graz, Austria: 1993.

McFarland D., Sarnacki W., Wolpaw J. EEG-based brain-computer interface (BCI): Multiple selections with one dimensional control. Soc. Neurosci. Abstr. 1998;23:656.

Pfurtscheller G., Flotzinger D., Kalcher J. Brain-computer interface—A new communication device for handicapped persons. J. Microcomput. Appl. 1993;16:293–299. doi: 10.1006/jmca.1993.1030. DOI

Cecotti H. Spelling with non-invasive Brain–Computer Interfaces–Current and future trends. J. Physiol.-Paris. 2011;105:106–114. doi: 10.1016/j.jphysparis.2011.08.003. PubMed DOI

DEL R. MILLÁN J., Ferrez P.W., Galán F., Lew E., Chavarriaga R. Non-invasive brain-machine interaction. Int. J. Pattern Recognit. Artif. Intell. 2008;22:959–972.

Tangermann M.W., Krauledat M., Grzeska K., Sagebaum M., Vidaurre C., Blankertz B., Müller K.R. Proceedings of the 21st International Conference on Neural Information Processing Systems. Curran Associates Inc.; Nice, France: 2008. Playing pinball with non-invasive BCI; pp. 1641–1648.

McFarland D.J., Lefkowicz A.T., Wolpaw J.R. Design and operation of an EEG-based brain-computer interface with digital signal processing technology. Behav. Res. Methods Instrum. Comput. 1997;29:337–345. doi: 10.3758/BF03200585. DOI

Miner L.A., McFarland D.J., Wolpaw J.R. Answering questions with an electroencephalogram-based brain-computer interface. Arch. Phys. Med. Rehabil. 1998;79:1029–1033. doi: 10.1016/S0003-9993(98)90165-4. PubMed DOI

Schalk G., McFarland D.J., Hinterberger T., Birbaumer N., Wolpaw J.R. BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 2004;51:1034–1043. doi: 10.1109/TBME.2004.827072. PubMed DOI

Millán J.D.R., Renkens F., Mourino J., Gerstner W. Non-invasive brain-actuated control of a mobile robot; Proceedings of the 18th International Joint Conference on Artificial Intelligence; Acapulco, Mexico. 9–15 August 2003; number CONF.

Kapgate D. Future of EEG Based Hybrid Visual Brain Computer Interface Systems in Rehabilitation of People with Neurological Disorders. Int. Res. J. Adv. Sci. Hub. 2020;2:15–20. doi: 10.47392/irjash.2020.31. DOI

Pfurtscheller G., Neuper C., Guger C., Harkam W., Ramoser H., Schlogl A., Obermaier B., Pregenzer M. Current trends in Graz brain-computer interface (BCI) research. IEEE Trans. Rehabil. Eng. 2000;8:216–219. doi: 10.1109/86.847821. PubMed DOI

Blankertz B., Dornhege G., Krauledat M., Müller K.R., Curio G. The non-invasive Berlin brain–computer interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. 2007;37:539–550. doi: 10.1016/j.neuroimage.2007.01.051. PubMed DOI

Vaughan T.M., McFarland D.J., Schalk G., Sarnacki W.A., Krusienski D.J., Sellers E.W., Wolpaw J.R. The wadsworth BCI research and development program: At home with BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 2006;14:229–233. doi: 10.1109/TNSRE.2006.875577. PubMed DOI

Pfurtscheller G., Neuper C., Muller G., Obermaier B., Krausz G., Schlogl A., Scherer R., Graimann B., Keinrath C., Skliris D., et al. Graz-BCI: State of the art and clinical applications. IEEE Trans. Neural Syst. Rehabil. Eng. 2003;11:1–4. doi: 10.1109/TNSRE.2003.814454. PubMed DOI

del R Millan J., Mouriño J., Franzé M., Cincotti F., Varsta M., Heikkonen J., Babiloni F. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Netw. 2002;13:678–686. doi: 10.1109/TNN.2002.1000132. PubMed DOI

Cincotti F., Mattia D., Aloise F., Bufalari S., Schalk G., Oriolo G., Cherubini A., Marciani M.G., Babiloni F. Non-invasive brain–computer interface system: Towards its application as assistive technology. Brain Res. Bull. 2008;75:796–803. doi: 10.1016/j.brainresbull.2008.01.007. PubMed DOI PMC

Schembri P., Pelc M., Ma J. The effect that auditory distractions have on a visual P300 speller while utilizing low-cost off-the-shelf equipment. Computers. 2020;9:68. doi: 10.3390/computers9030068. DOI

Schembri P., Pelc M., Ma J. The Effect that Auxiliary Taxonomized Auditory Distractions have on a P300 Speller while utilising Low Fidelity Equipment; Proceedings of the 2019 11th Computer Science and Electronic Engineering (CEEC); Colchester, UK. 18–20 September 2019; pp. 118–123.

Allison B.Z., Kübler A., Jin J. 30+ years of P300 brain–computer interfaces. Psychophysiology. 2020;57:e13569. doi: 10.1111/psyp.13569. PubMed DOI

Ravi A., Beni N.H., Manuel J., Jiang N. Comparing user-dependent and user-independent training of CNN for SSVEP BCI. J. Neural Eng. 2020;17:026028. doi: 10.1088/1741-2552/ab6a67. PubMed DOI

Li K., Sankar R., Arbel Y., Donchin E. Single trial independent component analysis for P300 BCI system; Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Minneapolis, MN, USA. 3–6 September 2009; pp. 4035–4038. PubMed

Jin J., Allison B.Z., Kaufmann T., Kübler A., Zhang Y., Wang X., Cichocki A. The changing face of P300 BCIs: A comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement. PLoS ONE. 2012;7:e49688. doi: 10.1371/journal.pone.0049688. PubMed DOI PMC

Fouad I.A., Labib F.E.Z.M., Mabrouk M.S., Sharawy A.A., Sayed A.Y. Improving the performance of P300 BCI system using different methods. Netw. Model. Anal. Health Inform. Bioinform. 2020;9:1–13. doi: 10.1007/s13721-020-00268-1. DOI

Eidel M., Kübler A. Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability. Front. Hum. Neurosci. 2020;14:265. doi: 10.3389/fnhum.2020.00265. PubMed DOI PMC

Liu B., Huang X., Wang Y., Chen X., Gao X. BETA: A Large Benchmark Database Toward SSVEP-BCI Application. Front. Neurosci. 2020;14:627. doi: 10.3389/fnins.2020.00627. PubMed DOI PMC

Chailloux Peguero J.D., Mendoza-Montoya O., Antelis J.M. Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions. Sensors. 2020;20:7198. doi: 10.3390/s20247198. PubMed DOI PMC

Berlad I., Pratt H. P300 in response to the subject’s own name. Electroencephalogr. Clin. Neurophysiol. Potentials Sect. 1995;96:472–474. doi: 10.1016/0168-5597(95)00116-A. PubMed DOI

Polich J., Margala C. P300 and probability: Comparison of oddball and single-stimulus paradigms. Int. J. Psychophysiol. 1997;25:169–176. doi: 10.1016/S0167-8760(96)00742-8. PubMed DOI

Dutt-Mazumder A., Huggins J.E. Performance comparison of a non-invasive P300-based BCI mouse to a head-mouse for people with SCI. Brain-Comput. Interfaces. 2020;7:1–10. doi: 10.1080/2326263X.2020.1716532. DOI

Cortez S.A., Flores C., Andreu-Perez J. Proceedings of the 5th Brazilian Technology Symposium. Springer; Berlin/Heidelberg, Germany: 2020. A Smart Home Control Prototype Using a P300-Based Brain–Computer Interface for Post-stroke Patients; pp. 131–139.

Bulat M., Karpman A., Samokhina A., Panov A. Playing a P300-BCI VR game based leads to changes in cognitive function of healthy adults. bioRxiv. 2020 doi: 10.1101/2020.05.28.118281. DOI

Mouli S., Palaniappan R., Molefi E., McLoughlin I. In-Ear Electrode EEG for Practical SSVEP BCI. Technologies. 2020;8:63. doi: 10.3390/technologies8040063. DOI

Peters B., Bedrick S., Dudy S., Eddy B., Higger M., Kinsella M., McLaughlin D., Memmott T., Oken B., Quivira F., et al. SSVEP BCI and eye tracking use by individuals with late-stage ALS and visual impairments. Front. Hum. Neurosci. 2020;14:457. doi: 10.3389/fnhum.2020.595890. PubMed DOI PMC

Hwang H.J., Lim J.H., Jung Y.J., Choi H., Lee S.W., Im C.H. Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard. J. Neurosci. Methods. 2012;208:59–65. doi: 10.1016/j.jneumeth.2012.04.011. PubMed DOI

Muller-Putz G.R., Pfurtscheller G. Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans. Biomed. Eng. 2007;55:361–364. doi: 10.1109/TBME.2007.897815. PubMed DOI

Horki P., Solis-Escalante T., Neuper C., Müller-Putz G. Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb. Med. Biol. Eng. Comput. 2011;49:567–577. doi: 10.1007/s11517-011-0750-2. PubMed DOI

Chen X., Zhao B., Wang Y., Gao X. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. J. Neural Eng. 2019;16:026012. doi: 10.1088/1741-2552/aaf594. PubMed DOI

Lin J.S., Jiang Z.Y. Implementing remote presence using quadcopter control by a non-invasive BCI device. Comput. Sci. Inf. Technol. 2015;3:122–126. doi: 10.13189/csit.2015.030405. DOI

Cho J.H., Jeong J.H., Shim K.H., Kim D.J., Lee S.W. Classification of hand motions within EEG signals for non-invasive BCI-based robot hand control; Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC); Miyazaki, Japan. 7–10 October 2018; pp. 515–518.

Hiremath S.V., Chen W., Wang W., Foldes S., Yang Y., Tyler-Kabara E.C., Collinger J.L., Boninger M.L. Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays. Front. Integr. Neurosci. 2015;9:40. doi: 10.3389/fnint.2015.00040. PubMed DOI PMC

Angelakis E., Hatzis A., Panourias I., Sakas D. Operative Neuromodulation. Springer; Berlin/Heidelberg, Germany: 2007. Brain-computer interface: A reciprocal self-regulated neuromodulation; pp. 555–559. PubMed

Sorger B., Goebel R. Handbook of Clinical Neurology. Volume 168. Elsevier; Berlin/Heidelberg, Germany: 2020. Real-time fMRI for brain-computer interfacing; pp. 289–302. PubMed

Weiskopf N., Mathiak K., Bock S.W., Scharnowski F., Veit R., Grodd W., Goebel R., Birbaumer N. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) IEEE Trans. Biomed. Eng. 2004;51:966–970. doi: 10.1109/TBME.2004.827063. PubMed DOI

Sitaram R., Veit R., Stevens B., Caria A., Gerloff C., Birbaumer N., Hummel F. Acquired control of ventral premotor cortex activity by feedback training: An exploratory real-time FMRI and TMS study. Neurorehabilit. Neural Repair. 2012;26:256–265. doi: 10.1177/1545968311418345. PubMed DOI

Rota G., Handjaras G., Sitaram R., Birbaumer N., Dogil G. Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing. Brain Lang. 2011;117:123–132. doi: 10.1016/j.bandl.2010.07.008. PubMed DOI

Sitaram R., Weiskopf N., Caria A., Veit R., Erb M., Birbaumer N. fMRI brain-computer interfaces. IEEE Signal Process. Mag. 2007;25:95–106. doi: 10.1109/MSP.2008.4408446. DOI

Liberati G., Veit R., Kim S., Birbaumer N., Von Arnim C., Jenner A., Lulé D., Ludolph A.C., Raffone A., Belardinelli M.O., et al. Development of a binary fMRI-BCI for Alzheimer patients: A semantic conditioning paradigm using affective unconditioned stimuli; Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction; Geneva, Switzerland. 2–5 September 2013; pp. 838–842.

Simon J., Fishbein P., Zhu L., Roberts M., Martin I. Neural Interface Engineering. Springer; Berlin/Heidelberg, Germany: 2020. Functional Magnetic Resonance Imaging-Based Brain Computer Interfaces; pp. 17–47.

Rieke J.D., Matarasso A.K., Yusufali M.M., Ravindran A., Alcantara J., White K.D., Daly J.J. Development of a Combined, Sequential Real-Time fMRI and fNIRS Neurofeedback System Enhance Motor Learning After Stroke. J. Neurosci. Methods. 2020;341:108719. doi: 10.1016/j.jneumeth.2020.108719. PubMed DOI

Almulla L., Al-Naib I., Althobaiti M. Hemodynamic responses during standing and sitting activities: A study toward fNIRS-BCI. Biomed. Phys. Eng. Express. 2020;6:055005. doi: 10.1088/2057-1976/aba102. PubMed DOI

Nazeer H., Naseer N., Khan R.A., Noori F.M., Qureshi N.K., Khan U.S., Khan M.J. Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis. J. Neural Eng. 2020;17:056025. doi: 10.1088/1741-2552/abb417. PubMed DOI

Mandal S., Singh B., Thakur K. Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm. Electron. Lett. 2020;56:1386–1389. doi: 10.1049/el.2020.2710. DOI

Ghonchi H., Fateh M., Abolghasemi V., Ferdowsi S., Rezvani M. Spatio-temporal deep learning for EEG-fNIRS brain computer interface; Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); Montreal, QC, Canada. 20–24 July 2020; pp. 124–127. PubMed

Li F., Tao Q., Peng W., Zhang T., Si Y., Zhang Y., Yi C., Biswal B., Yao D., Xu P. Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting-to task-state: Evidence from a simultaneous event-related EEG-fMRI study. NeuroImage. 2020;205:116285. doi: 10.1016/j.neuroimage.2019.116285. PubMed DOI

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. doi: 10.1093/neuros/nyz286. PubMed DOI

Jerbi K., Vidal J., Mattout J., Maby E., Lecaignard F., Ossandon T., Hamamé C., Dalal S., Bouet R., Lachaux J.P., et al. Inferring hand movement kinematics from MEG, EEG and intracranial EEG: From brain-machine interfaces to motor rehabilitation. Irbm. 2011;32:8–18. doi: 10.1016/j.irbm.2010.12.004. DOI

LaRocco J., Le M.D., Paeng D.G. A systemic review of available low-cost EEG headsets used for drowsiness detection. Front. Neuroinform. 2020;14:42. doi: 10.3389/fninf.2020.553352. PubMed DOI PMC

de Lissa P., Sörensen S., Badcock N., Thie J., McArthur G. Measuring the face-sensitive N170 with a gaming EEG system: A validation study. J. Neurosci. Methods. 2015;253:47–54. doi: 10.1016/j.jneumeth.2015.05.025. PubMed DOI

Doudou M., Bouabdallah A., Cherfaoui V. A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System. Sens. Transducers J. 2018;224:39–50.

Aghaei-Lasboo A., Inoyama K., Fogarty A.S., Kuo J., Meador K.J., Walter J.J., Le S.T., Graber K.D., Razavi B., Fisher R.S. Tripolar concentric EEG electrodes reduce noise. Clin. Neurophysiol. 2020;131:193–198. doi: 10.1016/j.clinph.2019.10.022. PubMed DOI

Liu X., Makeyev O., Besio W. Improved Spatial Resolution of Electroencephalogram Using Tripolar Concentric Ring Electrode Sensors. J. Sens. 2020;2020:6269394. doi: 10.1155/2020/6269394. DOI

g.tec Medical Engineering | Brain-Computer Interfaces and Neurotechnology. [(accessed on 29 October 2020)]; Available online: https://www.gtec.at/

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. doi: 10.1080/10447318.2019.1612213. DOI

Chi Y.M., Wang Y.T., Wang Y., Maier C., Jung T.P., Cauwenberghs G. Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2011;20:228–235. doi: 10.1109/TNSRE.2011.2174652. PubMed DOI

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. arXiv. 20202001.11337 PubMed

Belkacem A.N., Jamil N., Palmer J.A., Ouhbi S., Chen C. Brain computer interfaces for improving the quality of life of older adults and elderly patients. Front. Neurosci. 2020;14:692. doi: 10.3389/fnins.2020.00692. PubMed DOI PMC

OpenBCI—Open Source Biosensing Tools (EEG, EMG, EKG, and more) [(accessed on 27 October 2020)]; Available online: https://openbci.com/

EMOTIV | Brain Data Measuring Hardware and Software Solutions. [(accessed on 27 October 2020)]; Available online: https://www.emotiv.com/

Muse™—Meditation Made Easy with the Muse Headband. [(accessed on 27 October 2020)]; Available online: https://choosemuse.com/

Stytsenko K., Jablonskis E., Prahm C. MEi: CogSci Conference 2011. Universitat Wien; Ljubljana, Slovenia: 2011. Evaluation of consumer EEG device Emotiv EPOC.

Liu Y., Jiang X., Cao T., Wan F., Mak P.U., Mak P.I., Vai M.I. Implementation of SSVEP based BCI with Emotiv EPOC; Proceedings of the 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings; Tianjin, China. 2–4 July 2012; pp. 34–37.

EEG—ECG—Biosensors. [(accessed on 27 October 2020)]; Available online: http://neurosky.com/

Crowley K., Sliney A., Pitt I., Murphy D. Evaluating a brain-computer interface to categorise human emotional response; Proceedings of the 2010 10th IEEE International Conference on Advanced Learning Technologies; Sousse, Tunisia. 5–7 July 2010; pp. 276–278.

Lakhan P., Banluesombatkul N., Changniam V., Dhithijaiyratn R., Leelaarporn P., Boonchieng E., Hompoonsup S., Wilaiprasitporn T. Consumer grade brain sensing for emotion recognition. IEEE Sens. J. 2019;19:9896–9907. doi: 10.1109/JSEN.2019.2928781. DOI

Frey J. Comparison of a consumer grade EEG amplifier with medical grade equipment in BCI applications; Proceedins of the 2016 6th International BCI Meeting – BCI Past, Present and Future, Asilomar Conference Center; Pacific Grove, CA, USA. 30 May–3 June 2016.

Frey J. Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications. arXiv. 20161606.02438

Haddix C., Bahrani A.A., Kawala-Janik A., Besio W.G., Yu G., Sunderam S. Trial measurement of movement-related cortical dynamics using electroencephalography and diffuse correlation spectroscopy; Proceedings of the 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR); Miedzyzdroje, Poland. 28–31 August 2017; pp. 642–645.

Makeyev O., Ding Q., Kay S.M., Besio W.G. Sensor integration of multiple tripolar concentric ring electrodes improves pentylenetetrazole-induced seizure onset detection in rats; Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; San Diego, CA, USA. 28 August–1 September 2012; pp. 5154–5157. PubMed

Makeyev O., Ding Q., Martínez-Juárez I.E., Gaitanis J., Kay S.M., Besio W.G. Multiple sensor integration for seizure onset detection in human patients comparing conventional disc versus novel tripolar concentric ring electrodes; Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Osaka, Japan. 3–7 July 2013; pp. 17–20. PubMed

Müller-Putz G.R., Scherer R., Pfurtscheller G., Rupp R. EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci. Lett. 2005;382:169–174. doi: 10.1016/j.neulet.2005.03.021. PubMed DOI

Kuś R., Duszyk A., Milanowski P., Łabęcki M., Bierzyńska M., Radzikowska Z., Michalska M., Żygierewicz J., Suffczyński P., Durka P.J. On the quantification of SSVEP frequency responses in human EEG in realistic BCI conditions. PLoS ONE. 2013;8:e77536. doi: 10.1371/journal.pone.0077536. PubMed DOI PMC

Volosyak I., Valbuena D., Malechka T., Peuscher J., Gräser A. Brain–computer interface using water-based electrodes. J. Neural Eng. 2010;7:066007. doi: 10.1088/1741-2560/7/6/066007. PubMed DOI

Chabuda A., Durka P., Żygierewicz J. High frequency SSVEP-BCI with hardware stimuli control and phase-synchronized comb filter. IEEE Trans. Neural Syst. Rehabil. Eng. 2017;26:344–352. doi: 10.1109/TNSRE.2017.2734164. PubMed DOI

Tung S.W., Guan C., Ang K.K., Phua K.S., Wang C., Zhao L., Teo W.P., Chew E. Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis; Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Osaka, Japan. 3–7 July 2013; pp. 261–264. PubMed

Onose G., Grozea C., Anghelescu A., Daia C., Sinescu C., Ciurea A., Spircu T., Mirea A., Andone I., Spânu A., et al. 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. doi: 10.1038/sc.2012.14. PubMed DOI

Katona J., Kovari A. The evaluation of bci and pebl-based attention tests. Acta Polytech. Hung. 2018;15:225–249.

Fazli S., Danóczy M., Popescu F., Blankertz B., Müller K.R. International Work-Conference on Artificial Neural Networks. Springer; Berlin/Heidelberg, Germany: 2009. Using rest class and control paradigms for brain computer interfacing; pp. 651–665.

Bancaud J., Dell M. Technics and method of stereotaxic functional exploration of the brain structures in man (cortex, subcortex, central gray nuclei) Rev. Neurol. 1959;101:213. PubMed

Herff C., Krusienski D.J., Kubben P. The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions. Front. Neurosci. 2020;14:123. doi: 10.3389/fnins.2020.00123. PubMed DOI PMC

Guenot M., Isnard J., Ryvlin P., Fischer C., Ostrowsky K., Mauguiere F., Sindou M. Neurophysiological monitoring for epilepsy surgery: The Talairach SEEG method. Stereotact. Funct. Neurosurg. 2001;77:29–32. doi: 10.1159/000064595. PubMed DOI

Allen P., Fish D., Smith S. Very high-frequency rhythmic activity during SEEG suppression in frontal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 1992;82:155–159. doi: 10.1016/0013-4694(92)90160-J. PubMed DOI

Sharma A., Rai J.K., Tewari R.P. Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region. Biomed. Eng. Tech. 2020;65:705–720. doi: 10.1515/bmt-2020-0044. PubMed DOI

Chandrasekaran S., Bickel S., Herrero J.L., Kim J.W., Markowitz N., Espinal E., Bhagat N.A., Ramdeo R., Xu J., Glasser M.F., et al. Evoking highly focal percepts in the fingertips through targeted stimulation of sulcal regions of the brain for sensory restoration. medRxiv. 2020 doi: 10.1515/bmt-2020-0044. PubMed DOI PMC

Talukdar U., Hazarika S.M., Gan J.Q. Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI. Biomed. Signal Process. Control. 2020;58:101829. doi: 10.1016/j.bspc.2019.101829. DOI

Wong C.M., Wang B., Wang Z., Lao K.F., Rosa A., Wan F. Spatial Filtering in SSVEP-based BCIs: Unified Framework and New Improvements. IEEE Trans. Biomed. Eng. 2020;67:3057–3072. doi: 10.1109/TBME.2020.2975552. PubMed DOI

Gaber A., Ghazali M. Trends in Brain Computer Interfaces. EURASIP J. Adv. Signal Process. 2005;2005:861614.

Zander T.O., Kothe C., Jatzev S., Gaertner M. Brain-Computer Interfaces. Springer; Berlin/Heidelberg, Germany: 2010. Enhancing human-computer interaction with input from active and passive brain-computer interfaces; pp. 181–199.

Andreessen L.M., Gerjets P., Meurers D., Zander T.O. Toward neuroadaptive support technologies for improving digital reading: A passive BCI-based assessment of mental workload imposed by text difficulty and presentation speed during reading. User Model. User-Adapt. Interact. 2020:1–30. doi: 10.1007/s11257-020-09273-5. DOI

Elsawy A.S., Eldawlatly S., Taher M., Aly G.M. MindEdit: A P300-based text editor for mobile devices. Comput. Biol. Med. 2017;80:97–106. doi: 10.1016/j.compbiomed.2016.11.014. PubMed DOI

Jijun T., Peng Z., Ran X., Lei D. The portable P300 dialing system based on tablet and Emotiv Epoc headset; Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy. 25–29 August 2015; pp. 566–569. PubMed

Tahmasebzadeh A., Bahrani M., Setarehdan S.K. Development of a robust method for an online P300 Speller Brain Computer Interface; Proceedings of the 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER); San Diego, CA, USA. 6–8 November 2013; pp. 1070–1075.

Meshriky M.R., Eldawlatly S., Aly G.M. An intermixed color paradigm for P300 spellers: A comparison with gray-scale spellers; Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS); Thessaloniki, Greece. 22–24 June 2017; pp. 242–247.

Browarska N., Kawala-Sterniuk A., Zygarlicki J. Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain—Computer interfaces: Choosing the appropriate filtering method. Bio-Algorithms Med-Syst. 2020:20200051. doi: 10.1515/bams-2020-0051. DOI

Worthen-Chaudhari L.C., McNally M.P., Deshpande A., Bakaraju V. In-Home Neurogaming: Demonstrating the impact of valid gesture recognition method on high volume kinematic outcomes. J. Biomech. 2020;104:109726. doi: 10.1016/j.jbiomech.2020.109726. PubMed DOI

Beveridge R., Wilson S., Callaghan M., Coyle D. Neurogaming with motion-onset visual evoked potentials (mVEPs): Adults versus teenagers. IEEE Trans. Neural Syst. Rehabil. Eng. 2019;27:572–581. doi: 10.1109/TNSRE.2019.2904260. PubMed DOI

Putze F., Vourvopoulos A., Lécuyer A., Krusienski D., i Badia S.B., Mullen T., Herff C. Brain-Computer Interfaces and Augmented/Virtual Reality. Front. Hum. Neurosci. 2020;14:144. doi: 10.3389/fnhum.2020.00144. PubMed DOI PMC

Putze F., Weiß D., Vortmann L.M., Schultz T. Augmented Reality Interface for Smart Home Control using SSVEP-BCI and Eye Gaze; Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics; Bari, Italy. 6–9 October 2019.

Juarez D., Tur-Viñes V., Mengual A. Neuromarketing Applied to Educational Toy Packaging. Front. Psychol. 2020;11:2077. doi: 10.3389/fpsyg.2020.02077. PubMed DOI PMC

Nilashi M., Samad S., Ahmadi N., Ahani A., Abumalloh R.A., Asadi S., Abdullah R., Ibrahim O., Yadegaridehkordi E. Neuromarketing: A Review of Research and Implications for Marketing. J. Soft Comput. Decis. Support Syst. 2020;7:23–31.

Hsu L., Chen Y.J. Music and wine tasting: An experimental neuromarketing study. Br. Food J. 2019;122 doi: 10.1108/BFJ-06-2019-0434. DOI

Aldayel M., Ykhlef M., Al-Nafjan A. Deep Learning for EEG-Based Preference Classification in Neuromarketing. Appl. Sci. 2020;10:1525. doi: 10.3390/app10041525. DOI

Shahriari M., Feiz D., Zarei A., Kashi E. The meta-analysis of neuro-marketing studies: Past, present and future. Neuroethics. 2020;13:261–273. doi: 10.1007/s12152-019-09400-z. DOI

Luth T., Ojdanic D., Friman O., Prenzel O., Graser A. Low level control in a semi-autonomous rehabilitation robotic system via a brain-computer interface; Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics; Noordwijk, The Netherlands. 13–15 June 2007; pp. 721–728.

Xiong M., Hotter R., Nadin D., Patel J., Tartakovsky S., Wang Y., Patel H., Axon C., Bosiljevac H., Brandenberger A., et al. A Low-Cost, Semi-Autonomous Wheelchair Controlled by Motor Imagery and Jaw Muscle Activation; Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC); Bari, Italy. 6–9 October 2019; pp. 2180–2185.

Stephe S., Kumar T.J.K.V. Imagery Recognition of EEG Signal Using Cuckoo-Search Masking Empirical Mode Decomposition. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2019;8:2717–2720.

Zgallai W., Brown J.T., Ibrahim A., Mahmood F., Mohammad K., Khalfan M., Mohammed M., Salem M., Hamood N. Deep learning AI application to an EEG driven BCI smart wheelchair; Proceedings of the 2019 Advances in Science and Engineering Technology International Conferences (ASET); Dubai, UAE. 26 March–10 April 2019; pp. 1–5.

Rebsamen B., Guan C., Zhang H., Wang C., Teo C., Ang M.H., Burdet E. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 2010;18:590–598. doi: 10.1109/TNSRE.2010.2049862. PubMed DOI

Leaman J., La H.M. A comprehensive review of smart wheelchairs: Past, present, and future. IEEE Trans. Hum.-Mach. Syst. 2017;47:486–499. doi: 10.1109/THMS.2017.2706727. DOI

Murugappan M., Ramachandran N., Sazali Y. Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 2010;3:390. doi: 10.4236/jbise.2010.34054. DOI

Vortmann L.M., Putze F. Attention-Aware Brain Computer Interface to avoid Distractions in Augmented Reality; Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems; Honolulu, HI, USA. 25 April 2020.

Browarska N., Kawala-Sterniuk A., Chechelski P., Zygarlicki J. Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and brain-computer interface system: A case study. Bio-Algorithms Med-Syst. 2020;1 doi: 10.1515/bams-2020-0050. DOI

Kołodziej M., Tarnowski P., Sawicki D., Majkowski A., Rak R., Bala A., Pluta A. Fatigue Detection Caused by Office Work with the Use of EOG Signal. IEEE Sens. J. 2020;20:15213–15223. doi: 10.1109/JSEN.2020.3012404. DOI

Wolska A., Sawicki D., Nowak K., Wisełka M., Kołodziej M. Method of Acute Alertness Level Evaluation after Exposure to Blue and Red Light (based on EEG): Technical Aspects; Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2018); Seville, Spain. 20–21 September 2018; pp. 19–21.

Kubacki A., Jakubowski A. AIP Conference Proceedings. Volume 2029. AIP Publishing LLC; Melville, NY, USA: 2018. Controlling the industrial robot model with the hybrid BCI based on EOG and eye tracking; p. 020032.

Garcia A.P., Schjølberg I., Gale S. EEG control of an industrial robot manipulator; Proceedings of the 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom); Budapest, Hungary. 2–5 December 2013; pp. 39–44.

Mason C., Gadzicki K., Meier M., Ahrens F., Kluss T., Maldonado J., Putze F., Fehr T., Zetzsche C., Herrmann M., et al. From Human to Robot Everyday Activity; Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Las Vegas, NV, USA (Virtual). 25–29 October 2020; pp. 8997–9004.

Kosmyna N., Tarpin-Bernard F., Bonnefond N., Rivet B. Feasibility of BCI control in a realistic smart home environment. Front. Hum. Neurosci. 2016;10:416. doi: 10.3389/fnhum.2016.00416. PubMed DOI PMC

Saboor A., Rezeika A., Stawicki P., Gembler F., Benda M., Grunenberg T., Volosyak I. International Work-Conference on Artificial Neural Networks. Springer; Cham, Switzerland: 2017. SSVEP-based BCI in a smart home scenario; pp. 474–485.

Carabalona R., Grossi F., Tessadri A., Castiglioni P., Caracciolo A., de Munari I. Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home. Ergonomics. 2012;55:552–563. doi: 10.1080/00140139.2012.661083. PubMed DOI

Kim H.J., Lee M.H., Lee M. A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task; Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI); Gangwon, Korea. 26–28 February 2020; pp. 1–6.

Alrajhi W., Alaloola D., Albarqawi A. Smart home: Toward daily use of BCI-based systems; Proceedings of the 2017 International Conference on Informatics, Health & Technology (ICIHT); Riyadh, Saudi Arabia. 21–23 February 2017; pp. 1–5.

Ang K.K., Guan C. Brain–computer interface for neurorehabilitation of upper limb after stroke. Proc. IEEE. 2015;103:944–953. doi: 10.1109/JPROC.2015.2415800. DOI

Zieliński T.P. Cyfrowe Przetwarzanie Sygnałów: Od Teorii do Zastosowań. Wydawnictwa Komunikacji Łączności; Warsaw, Poland: 2005.

Miao G.J., Clements M.A. Digital Signal Processing and Statistical Classification. Artech House; Norwood, MA, USA: 2002.

Enderle J., Bronzino J. Introduction to Biomedical Engineering. Academic Press; Cambridge, MA, USA: 2012.

Kang H.J., Kawasawa Y.I., Cheng F., Zhu Y., Xu X., Li M., Sousa A.M., Pletikos M., Meyer K.A., Sedmak G., et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–489. doi: 10.1038/nature10523. PubMed DOI PMC

Kawala-Janik A., Pelc M., Podpora M. Method for EEG signals pattern recognition in embedded systems. Elektron. Elektrotechnika. 2015;21:3–9. doi: 10.5755/j01.eee.21.3.9918. DOI

Rodin E., Funke M., Berg P., Matsuo F. Magnetoencephalographic spikes not detected by conventional electroencephalography. Clin. Neurophysiol. 2004;115:2041–2047. doi: 10.1016/j.clinph.2004.04.002. PubMed DOI

Wang G., Worrell G., Yang L., Wilke C., He B. Interictal spike analysis of high-density EEG in patients with partial epilepsy. Clin. Neurophysiol. 2011;122:1098–1105. doi: 10.1016/j.clinph.2010.10.043. PubMed DOI PMC

Breitling C., Zaehle T., Dannhauer M., Tegelbeckers J., Flechtner H.H., Krauel K. Comparison between conventional and HD-tDCS of the right inferior frontal gyrus in children and adolescents with ADHD. Clin. Neurophysiol. 2020;131:1146–1154. doi: 10.1016/j.clinph.2019.12.412. PubMed DOI PMC

Alhaddad M.J. Common average reference (CAR) improves P300 speller. Int. J. Eng. Technol. 2012;2:21.

Laiho J. Master’s Thesis. Perustieteiden korkeakoulu; Espoo, Finland: 2020. Recognizing Thoughts from Bioelectric Patterns? A Brain-Computer Interface with Deep Learning.

Wang L., Huang W., Yang Z., Hu X., Zhang C. A method from offline analysis to online training for the brain-computer interface based on motor imagery and speech imagery. Biomed. Signal Process. Control. 2020;62:102100. doi: 10.1016/j.bspc.2020.102100. DOI

Grozea C., Voinescu C.D., Fazli S. Bristle-sensors—Low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications. J. Neural Eng. 2011;8:025008. doi: 10.1088/1741-2560/8/2/025008. PubMed DOI

Saab J., Battes B., Grosse-Wentrup M., Scherer R., Billinger M., Kreilinger A. Simultaneous EEG Recordings with Dry and Wet Electrodes in Motor-Imagery. Citeseer; University Park, PA, USA: 2011.

Klekowicz H. Doctoral Thesis. Warsaw University; Warsaw, Poland: 2012. Opis i Identyfikacja Struktur Przejściowych w Sygnale EEG.

Kutz M. Biomedical Engineering and Design Handbook. McGraw-Hill; New York, NY, USA: 2009.

Tumanski S. Principles of Electrical Measurement. CRC Press; Boca Raton, FL, USA: 2006.

Semmlow J.L., Griffel B. Biosignal and Medical Image Processing. CRC Press; Boca Raton, FL, USA: 2014.

Jiang X., Bian G.B., Tian Z. Removal of artifacts from EEG signals: A review. Sensors. 2019;19:987. doi: 10.3390/s19050987. PubMed DOI PMC

Chahid A., Laleg-Kirati T.M. Optimized Biosignals Decomposition and Denoising Using Schrodinger Operator. [(accessed on 27 October 2020)];2020 Available online: https://repository.kaust.edu.sa/handle/10754/662791.

Abtahi F., Seoane F., Lindecrantz K. Electrical bioimpedance spectroscopy in time-variant systems: Is undersampling always a problem? J. Electr. Bioimpedance. 2014;5:28–33. doi: 10.5617/jeb.801. DOI

Causevic E., Morley R.E., Wickerhauser M.V., Jacquin A.E. Fast wavelet estimation of weak biosignals. IEEE Trans. Biomed. Eng. 2005;52:1021–1032. doi: 10.1109/TBME.2005.846722. PubMed DOI

Bagchi S., Mitra S.K. The Nonuniform Discrete Fourier Transform and Its Applications in Signal Processing. Kluwer Academic Publishers; Dordrecht, The Netherlands: 1999.

Khan A. Digital Signal Processing Fundamentals. Firewall Media; New Delhi, India: 2005.

Baby Deepa V., Thangaraj P. A study on classification of EEG Data using the Filters. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2011;2:1–140.

Philips C.L. Signals, Systems, and Transforms. 3rd ed. Prentice Hall; Upper Saddle River, NJ, USA: 2003.

Oppenheim A., Willsky A., Young I. Signals and Systems. Prentice Hall; Upper Saddle River, NJ, USA: 1983.

Bruce E.N. Biomedical Signal Processing and Signal Modeling. John Wiley and Sons; Hoboken, NJ, USA: 2000.

Yang Y., Peng Z., Zhang W., Meng G. Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances. Mech. Syst. Signal Process. 2019;119:182–221. doi: 10.1016/j.ymssp.2018.07.039. DOI

Kang M., Jung J., Shin S., Kang K.H., Kim Y.T. Multi bio-signal based algorithm using EMD and FFT for stress analysis; Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE); Las Vegas, NV, USA. 4–6 January 2020; pp. 1–4.

Xizheng Z., Yin L., Wang W. Wavelet Time-frequency Analysis of Electro-encephalogram (EEG) Processing. Int. J. Adv. Comput. Sci. Appl. 2010;1 doi: 10.14569/IJACSA.2010.010501. DOI

Geetha G., Geethalakshmi S. Scrutinizing different techniques for artifact removal from EEG signals. Int. J. Eng. Sci. Technol. (IJEST) 2011;3:1167–1172.

George S.T., Subathra M., Sairamya N., Susmitha L., Premkumar M.J. Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern. Biomed. Eng. 2020;40:709–728. doi: 10.1016/j.bbe.2020.02.001. DOI

Teolis A. Computational Signal Processing with Wavelets. Birkaeuser Boston; Boston, MA, USA: 1998.

Kawala A., Khoma V., Zmarzły D., Sovyn Y. Use of wavelet transform for qualification of rheograms characteristic points. Przegląd Elektrotechniczny. 2008;84:132–133.

Nishad A., Upadhyay A., Reddy G.R.S., Bajaj V. Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform. Electron. Lett. 2020;56:1370–1372. doi: 10.1049/el.2020.2526. DOI

Desai R., Porob P., Rebelo P., Edla D.R., Bablani A. EEG Data Classification for Mental State Analysis Using Wavelet Packet Transform and Gaussian Process Classifier. Wirel. Pers. Commun. 2020;115:2149–2169. doi: 10.1007/s11277-020-07675-7. DOI

Moghavvemi M., Attaran A., Esfahani M.M. 5th Kuala Lumpur International Conference on Biomedical Engineering 2011. Springer; Berlin/Heidelberg, Germany: 2011. EEG artifact signals tracking and filtering in real time for command control application; pp. 503–506.

Supratak A., Wu C., Dong H., Sun K., Guo Y. Machine Learning for Health Informatics. Springer; Berlin/Heidelberg, Germany: 2016. Survey on feature extraction and applications of biosignals; pp. 161–182.

Maćkiewicz A., Ratajczak W. Principal components analysis (PCA) Comput. Geosci. 1993;19:303–342. doi: 10.1016/0098-3004(93)90090-R. DOI

Richardson M. Principal Component Analysis. [(accessed on 2 January 2021)]; Available online: http://people.maths.ox.ac.uk/richardsonm/SignalProcPCA.pdf.

Molla M.K.I., Islam M.R., Tanaka T., Rutkowski T.M. Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing. 2012;97:297–308. doi: 10.1016/j.neucom.2012.05.009. DOI

Domínguez-Jiménez J.A., Campo-Landines K.C., Martínez-Santos J., Delahoz E.J., Contreras-Ortiz S. A machine learning model for emotion recognition from physiological signals. Biomed. Signal Process. Control. 2020;55:101646. doi: 10.1016/j.bspc.2019.101646. DOI

Elkerdawy M., Elhalaby M., Hassan A., Maher M., Shawky D., Badawi A. Building Cognitive Profiles of Learners Using EEG; Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS); Irbid, Jordan. 7–9 April 2020; pp. 027–032.

Martinek R., Kahankova R., Jezewski J., Jaros R., Mohylova J., Fajkus M., Nedoma J., Janku P., Nazeran H. Comparative effectiveness of ICA and PCA in extraction of fetal ECG from abdominal signals: Toward non-invasive fetal monitoring. Front. Physiol. 2018;9:648. doi: 10.3389/fphys.2018.00648. PubMed DOI PMC

Jobst B.C., Bartolomei F., Diehl B., Frauscher B., Kahane P., Minotti L., Sharan A., Tardy N., Worrell G., Gotman J. Intracranial EEG in the 21st Century. Epilepsy Curr. 2020;20:180–188. doi: 10.1177/1535759720934852. PubMed DOI PMC

Reza M.S., Ma J. ICA and PCA integrated feature extraction for classification; Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP); Chengdu, China. 6–10 November 2016; pp. 1083–1088.

Pająk M., Muślewski Ł., Landowski B., Grządziela A. Fuzzy identification of the reliability state of the mine detecting ship propulsion system. Pol. Marit. Res. 2019;26:55–64.

Zadeh L.A. Fuzzy logic. Computer. 1988;21:83–93. doi: 10.1109/2.53. DOI

Al-Kadi D., Muhiuddin G. Bipolar fuzzy BCI-implicative ideals of BCI-algebras. Ann. Commun. Math. 2020;3:88–96.

Ghumman M.K., Singh S., Singh N., Jindal B. Optimization of parameters for improving the performance of EEG-based BCI system. J. Reliab. Intell. Environ. 2020:1–12. doi: 10.1007/s40860-020-00117-y. DOI

Abbasi H., Bennet L., Gunn A.J., Unsworth C.P. Robust wavelet stabilized ‘footprints of Uncertainty’ for fuzzy system classifiers to automatically detect sharp waves in the EEG after hypoxia ischemia. Int. J. Neural Syst. 2017;27:1650051. doi: 10.1142/S0129065716500519. PubMed DOI

Plerou A., Vlamou E., Papadopoulos V. EEG Signal Pattern Recognition Analysis: Fuzzy Logic Systems Ascendancy. Adv. Fuzzy Sets Syst. 2016;21:107. doi: 10.17654/FS021020107. DOI

Krishnamurthi R., Goyal M. Soft Computing and Signal Processing. Springer; Berlin/Heidelberg, Germany: 2019. Hybrid Neuro-fuzzy Method for Data Analysis of Brain Activity Using EEG Signals; pp. 165–173.

Jiang Y., Gu X., Ji D., Qian P., Xue J., Zhang Y., Zhu J., Xia K., Wang S. Smart Diagnosis: A Multiple-Source Transfer TSK Fuzzy System for EEG Seizure Identification. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 2020;16:1–21. doi: 10.1145/3340240. DOI

Zadeh L.A. Is there a need for fuzzy logic? Inf. Sci. 2008;178:2751–2779. doi: 10.1016/j.ins.2008.02.012. DOI

Osowski S., Cichocki A., Siwek K. MATLAB w Zastosowaniu do Obliczeń Obwodowych i Przetwarzania Sygnałów. Oficyna Wydawnicza Politechniki Warszawskiej; Warszawa, Poland: 2006.

Kawala-Janik A., Bauer W., Żołubak M., Baranowski J. Early-stage pilot study on using fractional-order calculus-based filtering for the purpose of analysis of electroencephalography signals. Stud. Log. Gramm. Rhetor. 2016;47:103–111. doi: 10.1515/slgr-2016-0049. DOI

Kawala-Janik A., Bauer W., Al-Bakri A., Haddix C., Yuvaraj R., Cichon K., Podraza W. Implementation of Low-Pass Fractional Filtering for the Purpose of Analysis of Electroencephalographic Signals; Proceedings of the Non-Integer Order Calculus and its Applications: 9th International Conference on Non-Integer Order Calculus and Its Applications; Łódź, Poland. 11 October 2018; Cham, Switzerland: Springer; 2017. p. 63.

Bauer W., Kawala-Janik A. Theory and Applications of Non-Integer Order Systems. Springer; Berlin/Heidelberg, Germany: 2017. Implementation of bi-fractional filtering on the arduino uno hardware platform; pp. 419–428.

Baranowski J., Bauer W., Zagórowska M., Piątek P. On digital realizations of non-integer order filters. Circuits Syst. Signal Process. 2016;35:2083–2107. doi: 10.1007/s00034-016-0269-8. DOI

Popović N.B., Miljković N., Šekara T.B. Electrogastrogram and electrocardiogram interference: Application of fractional order calculus and Savitzky-Golay filter for biosignals segregation; Proceedings of the 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH); East Sarajevo, Bosnia and Herzegovina. 18–20 March 2020; pp. 1–5.

Baranowski J., Piątek P., Kawala-Janik A., Zagórowska M., Bauer W., Dziwiński T. Advances in Modelling and Control of Non-Integer-Order Systems. Springer; Berlin/Heidelberg, Germany: 2015. Non-integer order filtration of electromyographic signals; pp. 231–237.

Awal M.A., Mostafa S.S., Ahmad M. Performance analysis of Savitzky-Golay smoothing filter using ECG signal. Int. J. Comput. Inf. Technol. 2011;1:24.

Trinh P.T., Brossier R., Métivier L., Virieux J., Wellington P. Bessel smoothing filter for spectral-element mesh. Geophys. J. Int. 2017;209:1489–1512. doi: 10.1093/gji/ggx103. DOI

Luo J., Ying K., He P., Bai J. Properties of Savitzky–Golay digital differentiators. Digit. Signal Process. 2005;15:122–136. doi: 10.1016/j.dsp.2004.09.008. DOI

Savitzky A., Golay M.J. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964;36:1627–1639. doi: 10.1021/ac60214a047. DOI

Guiñón J.L., Ortega E., García-Antón J., Pérez-Herranz V. Moving average and Savitzki-Golay smoothing filters using Mathcad; Proceedings of the 2007 International Conference on Engineering Education – ICEE 2007; Coimbra, Portugal. 3–7 September 2007; pp. 84–91. DOI

Grzechca D., Szczeponik A. Comparison of Filtering Methods for Enhanced Reliability of a Train Axle Counter System. Sensors. 2020;20:2754. doi: 10.3390/s20102754. PubMed DOI PMC

Choi G.H., Moon H.M., Pan S.B. Biometrics system technology trends based on biosignal. J. Digit. Converg. 2017;15:381–391. doi: 10.14400/JDC.2017.15.1.381. DOI

Tsoi A.C., So D., Sergejew A. Classification of electroencephalogram using artificial neural networks. Adv. Neural Inf. Process. Syst. 1994;6:1151–1158.

Ko W., Jeon E., Jeong S., Suk H.I. Multi-Scale Neural network for EEG Representation Learning in BCI. arXiv. 20202003.02657

Joseph A.F.A., Govindaraju C. Minimizing electrodes for effective brain computer interface. Biomed. Signal Process. Control. 2021;63:102201. doi: 10.1016/j.bspc.2020.102201. DOI

Szczęsna A., Błaszczyszyn M., Kawala A. Convolutional neural network in upper limb functional motion analysis after stroke. PeerJ. 2020 doi: 10.7717/peerj.10124. PubMed DOI PMC

Garcia-Moreno F.M., Bermudez-Edo M., Rodríguez-Fórtiz M.J., Garrido J.L. A CNN-LSTM Deep Learning Classifier for Motor Imagery EEG Detection Using a Low-invasive and Low-Cost BCI Headband; Proceedings of the 2020 16th International Conference on Intelligent Environments (IE); Madrid, Spain. 20–23 July 2020; pp. 84–91.

Sussillo D., Nuyujukian P., Fan J.M., Kao J.C., Stavisky S.D., Ryu S., Shenoy K. A recurrent neural network for closed-loop intracortical brain–machine interface decoders. J. Neural Eng. 2012;9:026027. doi: 10.1088/1741-2560/9/2/026027. PubMed DOI PMC

Sussillo D., Stavisky S.D., Kao J.C., Ryu S.I., Shenoy K.V. Making brain–machine interfaces robust to future neural variability. Nat. Commun. 2016;7:13749. doi: 10.1038/ncomms13749. PubMed DOI PMC

Skomrock N.D., Schwemmer M.A., Ting J.E., Trivedi H.R., Sharma G., Bockbrader M.A., Friedenberg D.A. A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent. Front. Neurosci. 2018;12:763. doi: 10.3389/fnins.2018.00763. PubMed DOI PMC

Chaudhary U., Birbaumer N., Ramos-Murguialday A. Brain–computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 2016;12:513. doi: 10.1038/nrneurol.2016.113. PubMed DOI

Sebastián-Romagosa M., Cho W., Ortner R., Murovec N., Von Oertzen T., Kamada K., Allison B.Z., Guger C. Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study. Front. Neurosci. 2020;14:591435. doi: 10.3389/fnins.2020.591435. PubMed DOI PMC

Mak J.N., Wolpaw J.R. Clinical applications of brain-computer interfaces: Current state and future prospects. IEEE Rev. Biomed. Eng. 2009;2:187–199. doi: 10.1109/RBME.2009.2035356. PubMed DOI PMC

Mowla M.R., Cano R.I., Dhuyvetter K.J., Thompson D.E. Affective Brain-Computer Interfaces: Choosing a Meaningful Performance Measuring Metric. Comput. Biol. Med. 2020;126:104001. doi: 10.1016/j.compbiomed.2020.104001. PubMed DOI

Sawangjai P., Hompoonsup S., Leelaarporn P., Kongwudhikunakorn S., Wilaiprasitporn T. Consumer grade eeg measuring sensors as research tools: A review. IEEE Sens. J. 2019;20:3996–4024. doi: 10.1109/JSEN.2019.2962874. DOI

Wierzgała P., Zapała D., Wojcik G.M., Masiak J. Most popular signal processing methods in motor-imagery BCI: A review and meta-analysis. Front. Neuroinform. 2018;12:78. doi: 10.3389/fninf.2018.00078. PubMed DOI PMC

Grübler G., Al-Khodairy A., Leeb R., Pisotta I., Riccio A., Rohm M., Hildt E. Psychosocial and ethical aspects in non-invasive EEG-based BCI research—a survey among BCI users and BCI professionals. Neuroethics. 2014;7:29–41. doi: 10.1007/s12152-013-9179-7. DOI

Schermer M. The mind and the machine. On the conceptual and moral implications of brain-machine interaction. Nanoethics. 2009;3:217. doi: 10.1007/s11569-009-0076-9. PubMed DOI PMC

Thinnes-Elker F., Iljina O., Apostolides J.K., Kraemer F., Schulze-Bonhage A., Aertsen A., Ball T. Intention concepts and brain-machine interfacing. Front. Psychol. 2012;3:455. doi: 10.3389/fpsyg.2012.00455. PubMed DOI PMC

High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?

Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation