As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

College of Engineering The University of Texas in Arlington Arlington TX 76019 USA

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

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

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Von Neumann J. The Computer and the Brain. Yale University Press; New Haven, CT, USA: 2012.

Gao S., Wang Y., Gao X., Hong B. Visual and auditory brain–computer interfaces. IEEE Trans. Biomed. Eng. 2014;61:1436–1447. PubMed

Chandra P. A Survey on Deep Learning its Architecture and Various Applications. Asia Pac. J. Neural Netw. Appl. 2017;1:7–12. doi: 10.21742/ajnnia.2017.1.2.02. DOI

Swanson L.W. Brain Architecture: Understanding the Basic Plan. Oxford University Press; Oxford, UK: 2012.

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

Gao X., Xu D., Cheng M., Gao S. A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural Syst. Rehabil. Eng. 2003;11:137–140. PubMed

Kiloh L.G., McComas A.J., Osselton J.W. Clinical Electroencephalography. 3rd ed. Butterworths; London, UK: 1972.

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

Coenen A., Zayachkivska O. Adolf Beck: A Pioneer in Electroencephalography in between Richard Caton and Hans Berger. Adv. Cogn. Psychol. 2013;9:216–221. doi: 10.5709/acp-0148-3. PubMed DOI PMC

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

Ball T., Kern M., Mutschler I., Aertsen A., Schulze-Bonhage A. Signal Quality of Simultaneously Recorded Invasive and Non-Invasive EEG. NeuroImage. 2009;46:708–716. doi: 10.1016/j.neuroimage.2009.02.028. PubMed DOI

Acharya J.N., Acharya V.J. Overview of EEG montages and principles of localization. J. Clin. Neurophysiol. 2019;36:325–329. doi: 10.1097/WNP.0000000000000538. PubMed DOI

Sazgar M., Young M.G. Absolute Epilepsy and EEG Rotation Review. Springer; Berlin, Germany: 2019. Overview of EEG, electrode placement, and montages; pp. 117–125.

Kutluay E., Kalamangalam G.P. Montages for Noninvasive EEG Recording. J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc. 2019;36:330. doi: 10.1097/WNP.0000000000000546. 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

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

Tomasz R. Brain–Robot and Speller Interfaces Using Spatial Multisensory Brain-Computer Interface Paradigms. Front. Comput. Neurosci. 2015;9 doi: 10.3389/conf.fncom.2015.56.00014. DOI

Allison B.Z., Neuper C. Could Anyone Use a BCI? In: Tan D.S., Nijholt A., editors. Brain-Computer Interfaces. Springer; London, UK: 2010. pp. 35–54. DOI

Cichocki A., Washizawa Y., Rutkowski T., Bakardjian H., Phan A.H., Choi S., Lee H., Zhao Q., Zhang L., Li Y. Noninvasive BCIs: Multiway Signal-Processing Array Decompositions. Computer. 2008;41:34–42. doi: 10.1109/MC.2008.431. DOI

Kawala-Sterniuk A., Browarska N., Al-Bakri A., Pelc M., Zygarlicki J., Sidikova M., Martinek R., Gorzelanczyk E.J. Summary of over Fifty Years with Brain-Computer Interfaces—A Review. Brain Sci. 2021;11:43. doi: 10.3390/brainsci11010043. 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

Oostenveld R., Praamstra P. The Five Percent Electrode System for High-Resolution EEG and ERP Measurements. Clin. Neurophysiol. 2001;112:713–719. doi: 10.1016/S1388-2457(00)00527-7. PubMed DOI

Ferree T.C., Luu P., Russell G.S., Tucker D.M. Scalp Electrode Impedance, Infection Risk, and EEG Data Quality. Clin. Neurophysiol. 2001;112:536–544. doi: 10.1016/S1388-2457(00)00533-2. PubMed DOI

Gopan K.G., Prabhu S.S., Sinha N. Sleep EEG Analysis Utilizing Inter-Channel Covariance Matrices. Biocybern. Biomed. Eng. 2020;40:527–545. doi: 10.1016/j.bbe.2020.01.013. DOI

Banville H., Chehab O., Hyvärinen A., Engemann D.A., Gramfort A. Uncovering the Structure of Clinical EEG Signals with Self-Supervised Learning. arXiv. 2020 doi: 10.1088/1741-2552/abca18.2007.16104 PubMed DOI

Rangayyan R.M. Biomedical Signal Analysis. 2nd ed. IEEE Press; Piscataway, NJ, USA: John Wiley & Sons, Inc; Hoboken, NJ, USA: 2015. (IEEE Press Series in Biomedical Engineering).

Penhaker M., Augustynek M. Zdravotnické Elektrické Přístroje 1. VSB—Technical University of Ostrava; Ostrava, Czechia: 2013.

Nyni K., Vincent L.K., Varghese L., Liya V., Johny A.N., Yesudas C. Wireless health monitoring system for ECG, EMG and EEG detecting; Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS); Coimbatore, India. 17–18 March 2017; Piscataway, NJ, USA: IEEE; 2017. pp. 1–5.

Fink A., Grabner R., Neuper C., Neubauer A. EEG Alpha Band Dissociation with Increasing Task Demands. Cogn. Brain Res. 2005;24:252–259. doi: 10.1016/j.cogbrainres.2005.02.002. PubMed DOI

Chervin R.D., Burns J.W., Subotic N.S., Roussi C., Thelen B., Ruzicka D.L. Correlates of Respiratory Cycle-Related EEG Changes in Children with Sleep-Disordered Breathing. Sleep. 2004;27:116–121. doi: 10.1093/sleep/27.1.116. PubMed DOI

van Albada S.J., Robinson P.A. Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Front. Hum. Neurosci. 2013;7 doi: 10.3389/fnhum.2013.00056. PubMed DOI PMC

Bruce E.N. Biomedical Signal Processing and Signal Modeling. Wiley; New York, NY, USA: 2001. (Wiley Series in Telecommunications and Signal Processing).

Sharma M., Patel S., Acharya U.R. Automated Detection of Abnormal EEG Signals Using Localized Wavelet Filter Banks. Pattern Recognit. Lett. 2020;133:188–194. doi: 10.1016/j.patrec.2020.03.009. DOI

Fernandez-Baca Vaca G., Park J.T. Focal EEG Abnormalities and Focal Ictal Semiology in Generalized Epilepsy. Seizure. 2020;77:7–14. doi: 10.1016/j.seizure.2019.12.013. PubMed DOI

Harris L., Angus-Leppan H. Epilepsy: Diagnosis, Classification and Management. Medicine. 2020;48:522–528. doi: 10.1016/j.mpmed.2020.05.001. DOI

Dalle Ave A.L., Bernat J.L. Inconsistencies Between the Criterion and Tests for Brain Death. J. Intensive Care Med. 2020;35:772–780. doi: 10.1177/0885066618784268. PubMed DOI

Emmady P.D., Anilkumar A.C. StatPearls. StatPearls Publishing; Treasure Island, FL, USA: 2020. EEG, Abnormal Waveforms. PubMed

Gurrala V., Yarlagadda P., Koppireddi P. Detection of Sleep Apnea Based on the Analysis of Sleep Stages Data Using Single Channel EEG. Trait. du Signal. 2021;38:431–436. doi: 10.18280/ts.380221. DOI

Jain S.V., Dye T., Kedia P. Value of combined video EEG and polysomnography in clinical management of children with epilepsy and daytime or nocturnal spells. Seizure. 2019;65:1–5. doi: 10.1016/j.seizure.2018.12.009. PubMed DOI

Melia U., Guaita M., Vallverdú M., Embid C., Vilaseca I., Salamero M., Santamaria J. Mutual information measures applied to EEG signals for sleepiness characterization. Med. Eng. Phys. 2015;37:297–308. doi: 10.1016/j.medengphy.2015.01.002. PubMed DOI

Buettner R., Fuhrmann J., Kolb L. Towards high-performance differentiation between Narcolepsy and Idiopathic Hypersomnia in 10 minute EEG recordings using a Novel Machine Learning Approach; Proceedings of the 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom); Bogota, Colombia. 14–16 October 2019; Piscataway, NJ, USA: IEEE; 2019. pp. 1–7.

Sarilar A.C., Ismailogullari S., Yilmaz R., Erdogan F.F., Per H. Electroencephalogram abnormalities in patients with NREM parasomnias. Sleep Med. 2019;77:256–260. doi: 10.1016/j.sleep.2019.05.009. PubMed DOI

Mishra S., Birok R. Recent Trends in Communication and Electronics. CRC Press; Boca Raton, FL, USA: 2021. Literature review: Sleep stage classification based on EEG signals using artificial intelligence technique; pp. 241–244.

Sunwoo J.S., Cha K.S., Byun J.I., Jun J.S., Kim T.J., Shin J.W., Lee S.T., Jung K.H., Park K.I., Chu K., et al. NREM sleep EEG oscillations in idiopathic REM sleep behavior disorder: A study of sleep spindles and slow oscillations. Sleep. 2020;44:zsaa160. doi: 10.1093/sleep/zsaa160. PubMed DOI

Nuwer M.R., Jordan S.E., Ahn S.S. Evaluation of Stroke Using EEG Frequency Analysis and Topographic Mapping. Neurology. 1987;37:1153. doi: 10.1212/WNL.37.7.1153. PubMed DOI

Juhasz Z. Quantitative Cost Comparison of On-Premise and Cloud Infrastructure Based EEG Data Processing. Clust. Comput. 2020;24:625–641. doi: 10.1007/s10586-020-03141-y. DOI

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 (IRJASH) 2020;2:15–20. doi: 10.47392/irjash.2020.31. DOI

Asadzadeh S., Yousefi Rezaii T., Beheshti S., Delpak A., Meshgini S. A Systematic Review of EEG Source Localization Techniques and Their Applications on Diagnosis of Brain Abnormalities. J. Neurosci. Methods. 2020;339:108740. doi: 10.1016/j.jneumeth.2020.108740. PubMed DOI

Maidana Capitán M., Cámpora N., Sigvard C.S., Kochen S., Samengo I. Time- and Frequency-Resolved Covariance Analysis for Detection and Characterization of Seizures from Intracraneal EEG Recordings. Biol. Cybern. 2020;114:461–471. doi: 10.1007/s00422-020-00840-y. PubMed DOI

Giuliano L., Mainieri G., Cicero C.E., Battaglia G., Guccione A., Salomone S., Drago F., Nicoletti A., Sofia V., Zappia M. Parasomnias, Sleep-Related Movement Disorders and Physiological Sleep Variants in Focal Epilepsy: A Polysomnographic Study. Seizure. 2020;81:84–90. doi: 10.1016/j.seizure.2020.07.026. PubMed DOI

Savadkoohi M., Oladunni T., Thompson L. A Machine Learning Approach to Epileptic Seizure Prediction Using Electroencephalogram (EEG) Signal. Biocybern. Biomed. Eng. 2020;40:1328–1341. doi: 10.1016/j.bbe.2020.07.004. PubMed DOI PMC

Reus E.E., Visser G.H., Cox F.M. Using Sampled Visual EEG Review in Combination with Automated Detection Software at the EMU. Seizure. 2020;80:96–99. doi: 10.1016/j.seizure.2020.06.002. PubMed DOI

Al-Bakri A.F., Villamar M.F., Haddix C., Bensalem-Owen M., Sunderam S. Noninvasive seizure prediction using autonomic measurements in patients with refractory epilepsy; Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Honolulu, HI, USA. 18–21 July 2018; Piscataway, NJ, USA: IEEE; 2018. pp. 2422–2425. PubMed

Cox F., Reus E., Widman G., Zwemmer J., Visser G. Epilepsy Monitoring Units Can Be Safe Places; a Prospective Study in a Large Cohort. Epilepsy Behav. 2020;102:106718. doi: 10.1016/j.yebeh.2019.106718. PubMed DOI

Duy P.Q., Krauss G.L., Crone N.E., Ma M., Johnson E.L. Antiepileptic Drug Withdrawal and Seizure Severity in the Epilepsy Monitoring Unit. Epilepsy Behav. 2020;109:107128. doi: 10.1016/j.yebeh.2020.107128. PubMed DOI

Askamp J., van Putten M.J. Mobile EEG in epilepsy. Int. J. Psychophysiol. 2014;91:30–35. doi: 10.1016/j.ijpsycho.2013.09.002. PubMed DOI

Gilliam F., Kuzniecky R., Faught E. Ambulatory EEG monitoring. J. Clin. Neurophysiol. 1999;16:111–115. doi: 10.1097/00004691-199903000-00003. PubMed DOI

Elger C.E., Hoppe C. Diagnostic challenges in epilepsy: Seizure under-reporting and seizure detection. Lancet Neurol. 2018;17:279–288. doi: 10.1016/S1474-4422(18)30038-3. PubMed DOI

Brunnhuber F., Slater J., Goyal S., Amin D., Thorvardsson G., Freestone D.R., Richardson M.P. Past, Present and Future of Home video-electroencephalographic telemetry: A review of the development of in-home video-electroencephalographic recordings. Epilepsia. 2020;61:S3–S10. doi: 10.1111/epi.16578. PubMed DOI

Mohammed N.S., Al-Mamoori M.J., Albermani A., AbudAlameer W.R., Abbas F.N. Electroencephalogram and Visual Evoked Potential Changes in Patients with Primary Headaches. Indian J. Forensic Med. Toxicol. 2020;14:1685–1692.

Somaiya S. Electroencephalogram (EEG): Meaning, Sources and Significance. 2016. [(accessed on 15 June 2021)]. Available online: https://www.biologydiscussion.com/human-physiology/electroencephalogram/electroencephalogram-eeg-meaning-sources-and-significance/62944?fbclid=IwAR0RNKnj2dBNPUABXEtPIxdoWuZIAFLOQYgW8vbqD7PYyrvzah22WGc9xhY.

Tatum W.O. Handbook of EEG Interpretation. DemosMedical; New York, NY, USA: 2014.

Chernecky C.C., Berger B.J., editors. Laboratory Tests and Diagnostic Procedures. 6th ed. Elsevier; St. Louis, MO, USA: 2013.

Cuellar M., Harkrider A., Jenson D., Thornton D., Bowers A., Saltuklaroglu T. Time–Frequency Analysis of the EEG Mu Rhythm as a Measure of Sensorimotor Integration in the Later Stages of Swallowing. Clin. Neurophysiol. 2016;127:2625–2635. doi: 10.1016/j.clinph.2016.04.027. PubMed DOI

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

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

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. 21–23 December 2018; pp. 1–7.

Das R., Chatterjee D., Das D., Sinharay A., Sinha A. Cognitive load measurement—A methodology to compare low cost commercial eeg devices; Proceedings of the 2014 International conference on advances in computing, communications and informatics (ICACCI); Delhi, India. 24–27 September 2014; Piscataway, NJ, USA: IEEE; 2014. pp. 1188–1194.

Stytsenko K., Jablonskis E., Prahm C. Evaluation of consumer EEG device Emotiv EPOC; Proceedings of the MEi: CogSci Conference 2011; Ljubljana, Slovenia. 17–18 June 2011.

Frey J. Comparison of a consumer grade EEG amplifier with medical grade equipment in BCI applications; Proceedings of the International BCI Meeting; Pacific Grove, CA, USA. 30 May–3 June 2016.

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

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

Ekandem J.I., Davis T.A., Alvarez I., James M.T., Gilbert J.E. Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics. 2012;55:592–598. doi: 10.1080/00140139.2012.662527. PubMed DOI

Sugiono S., Putra A.S., Fanani A.A., Cahyawati A.N., Oktavianty O. A New Concept of Product Design by Involving Emotional Factors Using Eeg: A Case Study of Xomputer Mouse Design. Acta Neuropsychol. 2021;19:63–80. doi: 10.5604/01.3001.0014.7021. DOI

Lacko D., Vleugels J., Fransen E., Huysmans T., De Bruyne G., Van Hulle M.M., Sijbers J., Verwulgen S. Ergonomic design of an EEG headset using 3D anthropometry. Appl. Ergon. 2017;58:128–136. doi: 10.1016/j.apergo.2016.06.002. PubMed DOI

Rogers J.M., Jensen J., Valderrama J.T., Johnstone S.J., Wilson P.H. Single-channel EEG measurement of engagement in virtual rehabilitation: A validation study. Virtual Real. 2021;25:357–366. doi: 10.1007/s10055-020-00460-8. DOI

Zhang J., Yao R., Ge W., Gao J. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput. Methods Programs Biomed. 2020;183:105089. doi: 10.1016/j.cmpb.2019.105089. PubMed DOI

Ko L.W., Komarov O., Lai W.K., Liang W.G., Jung T.P. Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task. J. Neural Eng. 2020;17:036015. doi: 10.1088/1741-2552/ab909f. PubMed DOI

Eldele E., Chen Z., Liu C., Wu M., Kwoh C.K., Li X., Guan C. An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 2021;29:809–818. doi: 10.1109/TNSRE.2021.3076234. PubMed DOI

Kaur J., Kaur A. A review on analysis of EEG signals; Proceedings of the 2015 International Conference on Advances in Computer Engineering and Applications; Ghaziabad, India. 19–20 March 2015; Piscataway, NJ, USA: IEEE; 2015. pp. 957–960.

Lee S., Buchsbaum M.S. Topographic Mapping of EEG Artifacts. Clin. EEG (Electroencephalogr.) 1987;18:61–67. PubMed

Durka P., Klekowicz H., Blinowska K., Szelenberger W., Niemcewicz S. A Simple System for Detection of EEG Artifacts in Polysomnographic Recordings. IEEE Trans. Biomed. Eng. 2003;50:526–528. doi: 10.1109/TBME.2003.809476. PubMed DOI

Moretti D. Computerized Processing of EEG–EOG–EMG Artifacts for Multi-Centric Studies in EEG Oscillations and Event-Related Potentials. Int. J. Psychophysiol. 2003;47:199–216. doi: 10.1016/S0167-8760(02)00153-8. PubMed DOI

Correa A.G., Laciar E., Patiño H., Valentinuzzi M. Artifact removal from EEG signals using adaptive filters in cascade. J. Phys. Conf. Ser. 2007;90:012081. doi: 10.1088/1742-6596/90/1/012081. DOI

Leske S., Dalal S.S. Reducing power line noise in EEG and MEG data via spectrum interpolation. Neuroimage. 2019;189:763–776. doi: 10.1016/j.neuroimage.2019.01.026. PubMed DOI PMC

La Rosa A.B., Pereira P.T., Ücker P., Paim G., da Costa E.A., Bampi S., Almeida S. Exploring NLMS-Based Adaptive Filter Hardware Architectures for Eliminating Power Line Interference in EEG Signals. Circuits Syst. Signal Process. 2021;40:3305–3337. doi: 10.1007/s00034-020-01620-6. DOI

Reddy A.G., Narava S. Artifact removal from EEG signals. Int. J. Comput. Appl. 2013;77:17–19.

Qian X., Xu Y.P., Li X. A CMOS continuous-time low-pass notch filter for EEG systems. Analog Integr. Circuits Signal Process. 2005;44:231–238. doi: 10.1007/s10470-005-3007-x. DOI

Saini M., Satija U., Upadhayay M.D. Effective Automated Method for Detection and Suppression of Muscle Artefacts from Single-Channel EEG Signal. Healthc. Technol. Lett. 2020;7:35–40. doi: 10.1049/htl.2019.0053. PubMed DOI PMC

Shah S.A.A., Zhang L., Bais A. Dynamical System Based Compact Deep Hybrid Network for Classification of Parkinson Disease Related EEG Signals. Neural Netw. 2020;130:75–84. doi: 10.1016/j.neunet.2020.06.018. PubMed DOI

Silva G., Alves M., Cunha R., Bispo B.C., Rodrigues P.M. Parkinson Disease Early Detection Using EEG Channels Cross-Correlation. Int. J. Appl. Eng. Res. 2020;15:197–203.

Noureddin B., Lawrence P.D., Birch G.E. Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker. IEEE Trans. Biomed. Eng. 2012;59:2103–2110. doi: 10.1109/TBME.2011.2108295. PubMed DOI

Jansen M., White T.P., Mullinger K.J., Liddle E.B., Gowland P.A., Francis S.T., Bowtell R., Liddle P.F. Motion-Related Artefacts in EEG Predict Neuronally Plausible Patterns of Activation in fMRI Data. NeuroImage. 2012;59:261–270. doi: 10.1016/j.neuroimage.2011.06.094. PubMed DOI PMC

Abbaspour H., Mehrshad N., Razavi S.M., Mesin L. Artefacts Removal to Detect Visual Evoked Potentials in Brain Computer Interface Systems. J. Biomimetics Biomater. Biomed. Eng. 2019;41:91–103. doi: 10.4028/www.scientific.net/JBBBE.41.91. DOI

Diykh M., Li Y., Abdulla S. EEG Sleep Stages Identification Based on Weighted Undirected Complex Networks. Comput. Methods Programs Biomed. 2020;184:105116. doi: 10.1016/j.cmpb.2019.105116. PubMed DOI

Jiao Y., Deng Y., Luo Y., Lu B.L. Driver Sleepiness Detection from EEG and EOG Signals Using GAN and LSTM Networks. Neurocomputing. 2020;408:100–111. doi: 10.1016/j.neucom.2019.05.108. DOI

Wang Y., Xu G., Zhang S., Luo A., Li M., Han C. EEG Signal Co-Channel Interference Suppression Based on Image Dimensionality Reduction and Permutation Entropy. Signal Process. 2017;134:113–122. doi: 10.1016/j.sigpro.2016.11.015. DOI

Benbadis S.R. EEG Artifacts: Overview, Physiologic Artifacts, Extraphysiologic Artifacts. 2019. [(accessed on 15 May 2021)]. Available online: https://emedicine.medscape.com/article/1140247-overview.

Tandle A., Jog N. Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques. Int. J. Comput. Appl. 2015;975:8887.

Cichocki A., Rutkowski T., Siwek K. Blind Signal Extraction of Signals with Specified Frequency Band; Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing; Martigny, Switzerland. 6 September 2002; pp. 515–524. DOI

Gallego-Jutglà E., Solé-Casals J., Rutkowski T.M., Cichocki A. Application of Multivariate Empirical Mode Decomposition for Cleaning Eye Blinks Artifacts from EEG Signals; Proceedings of the International Conference on Neural Computation Theory and Applications (Special Session on Challenges in Neuroengineering-2011); Paris, France. 24–26 October 2011; pp. 455–460.

Rakhmatulin I. Review of EEG Feature Selection by Neural Networks. Int. J. Sci. Bus. 2020;4:101–112. doi: 10.2139/ssrn.3675950. DOI

Baig M.Z., Aslam N., Shum H.P.H. Filtering Techniques for Channel Selection in Motor Imagery EEG Applications: A Survey. Artif. Intell. Rev. 2020;53:1207–1232. doi: 10.1007/s10462-019-09694-8. DOI

Higashi H., Rutkowski T.M., Tanaka T., Tanaka Y. Smoothing of xDAWN Spatial Filters for Robust Extraction of Event-Related Potentials; Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA); Jeju, Korea. 13–16 December 2016; pp. 1–5. DOI

Schlögl A., Anderer P., Roberts S.J., Pregenzer M., Pfurtscheller G. Artefact Detection in Sleep EEG by the Use of Kalman Filtering; Proceedings of the EMBEC’99; Vienna, Austria. 4–7 November 1999; pp. 1648–1649.

Li J., Chan S.C., Liu Z., Chang C. A Novel Adaptive Fading Kalman Filter-Based Approach to Time-Varying Brain Spectral/Connectivity Analyses of Event-Related EEG Signals. IEEE Access. 2020;8:51230–51245. doi: 10.1109/ACCESS.2020.2979551. DOI

Qi F., Wu W., Yu Z.L., Gu Z., Wen Z., Yu T., Li Y. Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification. IEEE Trans. Cybern. 2020;51:558–567. doi: 10.1109/TCYB.2019.2963709. PubMed DOI

Ahmed O.I., Yassin H.M., Said L.A., Psychalinos C., Radwan A.G. Implementation and Analysis of Tunable Fractional-Order Band-Pass Filter of Order 2α. AEU—Int. J. Electron. Commun. 2020;124:153343. doi: 10.1016/j.aeue.2020.153343. DOI

Baranowski J., Piatek P. Fractional Band-Pass Filters: Design, Implementation and Application to EEG Signal Processing. J. Circuits Syst. Comput. 2017;26:1750170. doi: 10.1142/S0218126617501705. DOI

Baranowski J., Bauer W., Zagorowska M., Piatek 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

Baranowski J., Pauluk M., Tutaj A. Analog Realization of Fractional Filters: Laguerre Approximation Approach. AEU—Int. J. Electron. Commun. 2017;81:1–11. doi: 10.1016/j.aeue.2017.06.011. DOI

Elwy O., Abdelaty A., Said L., Radwan A. Fractional Calculus Definitions, Approximations, and Engineering Applications. J. Eng. Appl. Sci. 2020;67:1–30.

Nagabushanam P., George S.T., Dolly D.R.J., Radha S. Artifact Cleaning of Motor Imagery EEG by Statistical Features Extraction Using Wavelet Families. Int. J. Circuit Theory Appl. 2020;48:2219–2241. doi: 10.1002/cta.2856. DOI

Bhati D., Sharma M., Pachori R.B., Gadre V.M. Time—Frequency Localized Three-Band Biorthogonal Wavelet Filter Bank Using Semidefinite Relaxation and Nonlinear Least Squares with Epileptic Seizure EEG Signal Classification. Digit. Signal Process. 2017;62:259–273. doi: 10.1016/j.dsp.2016.12.004. DOI

Bhattacharyya A., Sharma M., Pachori R.B., Sircar P., Acharya U.R. A Novel Approach for Automated Detection of Focal EEG Signals Using Empirical Wavelet Transform. Neural Comput. Appl. 2018;29:47–57. doi: 10.1007/s00521-016-2646-4. DOI

Mamun M., Al-Kadi M., Marufuzzaman M. Effectiveness of Wavelet Denoising on Electroencephalogram Signals. J. Appl. Res. Technol. 2013;11:156–160. doi: 10.1016/S1665-6423(13)71524-4. DOI

Yavuz E., Aydemir O. Olfaction Recognition by EEG Analysis Using Wavelet Transform Features; Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA); Sinaia, Romania. 2–5 August 2016; pp. 1–4. DOI

Ablin P., Cardoso J.F., Gramfort A. Spectral Independent Component Analysis with Noise Modeling for M/EEG Source Separation. arXiv. 20202008.09693 PubMed

Devulapalli S.P., Chanamallu S.R., Kodati S.P. A Hybrid ICA Kalman Predictor Algorithm for Ocular Artifacts Removal. Int. J. Speech Technol. 2020;23:727–735. doi: 10.1007/s10772-020-09721-y. DOI

Hsu S.H., Mullen T.R., Jung T.P., Cauwenberghs G. Real-time adaptive EEG source separation using online recursive independent component analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 2015;24:309–319. doi: 10.1109/TNSRE.2015.2508759. PubMed DOI PMC

Chang C.Y., Hsu S.H., Pion-Tonachini L., Jung T.P. Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings. IEEE Trans. Biomed. Eng. 2019;67:1114–1121. doi: 10.1109/TBME.2019.2930186. PubMed DOI

Shan X., Yang E.H., Zhou J., Chang V.W.C. Human-Building Interaction under Various Indoor Temperatures through Neural-Signal Electroencephalogram (EEG) Methods. Build. Environ. 2018;129:46–53. doi: 10.1016/j.buildenv.2017.12.004. DOI

Corradino C., Bucolo M. Automatic Preprocessing of EEG Signals in Long Time Scale; 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. 4110–4113. PubMed DOI

Li W., Shen Y., Zhang J., Huang X., Chen Y., Ge Y. Common Interferences Removal from Dense Multichannel EEG Using Independent Component Decomposition. Comput. Math. Methods Med. 2018;2018:1482874. doi: 10.1155/2018/1482874. PubMed DOI PMC

Arnin J., Kahani D., Lakany H., Conway B.A. Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications; Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Honolulu, HI, USA. 18–21 July 2018; pp. 235–238. PubMed DOI

Chaudhary U., Mrachacz-Kersting N., Birbaumer N. Neuropsychological and neurophysiological aspects of brain-computer-interface (BCI) control in paralysis. J. Physiol. 2020;599:2351–2359. doi: 10.1113/JP278775. PubMed DOI

Borgheai S.B., McLinden J., Zisk A.H., Hosni S.I., Deligani R.J., Abtahi M., Mankodiya K., Shahriari Y. Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System. IEEE Trans. Neural Syst. Rehabil. Eng. 2020;28:1198–1207. doi: 10.1109/TNSRE.2020.2980772. PubMed DOI PMC

Piccione F., Giorgi F., Tonin P., Priftis K., Giove S., Silvoni S., Palmas G., Beverina F. P300-Based Brain Computer Interface: Reliability and Performance in Healthy and Paralysed Participants. Clin. Neurophysiol. 2006;117:531–537. doi: 10.1016/j.clinph.2005.07.024. PubMed DOI

Khan O.I., Farooq F., Akram F., Choi M.T., Han S.M., Kim T.S. Robust Extraction of P300 Using Constrained ICA for BCI Applications. Med. Biol. Eng. Comput. 2012;50:231–241. doi: 10.1007/s11517-012-0861-4. PubMed DOI

Rashid M., Sulaiman N., Mustafa M., Khatun S., Bari B.S., Hasan M.J. Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems. In: Kasruddin Nasir A.N., Ahmad M.A., Najib M.S., Abdul Wahab Y., Othman N.A., Abd Ghani N.M., Irawan A., Khatun S., Raja Ismail R.M.T., Saari M.M., et al., editors. InECCE2019. Volume 632. Springer; Singapore: 2020. pp. 367–378. DOI

Pion-Tonachini L., Hsu S.H., Makeig S., Jung T.P., Cauwenberghs G. Real-time eeg source-mapping toolbox (rest): Online ica and source localization; Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy. 25–29 August 2015; Piscataway, NJ, USA: IEEE; 2015. pp. 4114–4117. PubMed PMC

Flandrin P., Rilling G., Goncalves P. Empirical Mode Decomposition as a Filter Bank. IEEE Signal Process. Lett. 2004;11:112–114. doi: 10.1109/LSP.2003.821662. DOI

Amo C., De Santiago L., Barea R., López-Dorado A., Boquete L. Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition. Sensors. 2017;17:989. doi: 10.3390/s17050989. PubMed DOI PMC

Chen S.J., Peng C.J., Chen Y.C., Hwang Y.R., Lai Y.S., Fan S.Z., Jen K.K. Comparison of FFT and Marginal Spectra of EEG Using Empirical Mode Decomposition to Monitor Anesthesia. Comput. Methods Programs Biomed. 2016;137:77–85. doi: 10.1016/j.cmpb.2016.08.024. PubMed DOI

Gaur P., Pachori R.B., Wang H., Prasad G. A Multi-Class EEG-Based BCI Classification Using Multivariate Empirical Mode Decomposition Based Filtering and Riemannian Geometry. Expert Syst. Appl. 2018;95:201–211. doi: 10.1016/j.eswa.2017.11.007. DOI

Rutkowski T.M., Mandic D.P., Cichocki A., Przybyszewski A.W. EMD Approach to Multichannel EEG Data—The Amplitude and Phase Components Clustering Analysis. J. Circuits Syst. Comput. 2010;19:215–229. doi: 10.1142/S0218126610006037. DOI

Molla M.K.I., Tanaka T., Rutkowski T.M., Cichocki A. Separation of EOG Artifacts from EEG Signals Using Bivariate EMD; Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing; Dallas, TX, USA. 14–19 March 2010; pp. 562–565. DOI

Maimon N.B., Molcho L., Intrator N., Lamy D. Single-Channel EEG Features during n-Back Task Correlate with Working Memory Load. arXiv. 20202008.04987

Leite N.M.N., Pereira E.T., Gurjão E.C., Veloso L.R. Deep convolutional autoencoder for eeg noise filtering; Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Madrid, Spain. 3–6 December 2018; Piscataway, NJ, USA: IEEE; 2018. pp. 2605–2612.

Yang B., Duan K., Fan C., Hu C., Wang J. Automatic ocular artifacts removal in EEG using deep learning. Biomed. Signal Process. Control. 2018;43:148–158. doi: 10.1016/j.bspc.2018.02.021. DOI

Jafarifarmand A., Badamchizadeh M.A. Artifacts removal in EEG signal using a new neural network enhanced adaptive filter. Neurocomputing. 2013;103:222–231. doi: 10.1016/j.neucom.2012.09.024. DOI

Cowan H., Daryanavard S., Porr B., Dahiya R. A real-time noise cancelling EEG electrode employing Deep Learning. arXiv. 20202011.03466

Quej V.H., Almorox J., Arnaldo J.A., Saito L. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J. Atmos. Sol.-Terr. Phys. 2017;155:62–70. doi: 10.1016/j.jastp.2017.02.002. DOI

Jang J.S. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. 1993;23:665–685. doi: 10.1109/21.256541. DOI

Chen W., Wang Z., Lao K.F., Wan F. Ocular artifact removal from EEG using ANFIS; Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); Beijing, China. 6–11 July 2014; Piscataway, NJ, USA: IEEE; 2014. pp. 2410–2417.

Pereira L.F., Patil S.A., Mahadeshwar C.D., Mishra I., D’Souza L. Artifact removal from EEG using ANFIS-GA; Proceedings of the 2016 Online International Conference on Green Engineering and Technologies (IC-GET); Coimbatore, India. 19 November 2016; Piscataway, NJ, USA: IEEE; 2016. pp. 1–6.

Akhtar M.T., James C.J. Focal artifact removal from ongoing EEG–a hybrid approach based on spatially-constrained ICA and wavelet de-noising; Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Minneapolis, MN, USA. 3–6 September 2009; Piscataway, NJ, USA: IEEE; 2009. pp. 4027–4030. PubMed

Akhtar M.T., Mitsuhashi W., James C.J. Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal Process. 2012;92:401–416. doi: 10.1016/j.sigpro.2011.08.005. DOI

Peng H., Hu B., Shi Q., Ratcliffe M., Zhao Q., Qi Y., Gao G. Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications. IEEE J. Biomed. Health Inform. 2013;17:600–607. doi: 10.1109/JBHI.2013.2253614. PubMed DOI

Maddirala A., Shaik R.A. Removal of EOG Artifacts from Single Channel EEG Signals Using Combined Singular Spectrum Analysis and Adaptive Noise Canceler. IEEE Sens. J. 2016;16:8279–8287. doi: 10.1109/JSEN.2016.2560219. DOI

Nguyen H.A.T., Le T.H., Bui T.D. A Deep Wavelet Sparse Autoencoder Method for Online and Automatic Electrooculographical Artifact Removal. Neural Comput. Appl. 2020;32:18255–18270. doi: 10.1007/s00521-020-04953-0. DOI

Kose M.R., Ahirwal M.K., Janghel R.R. Descendant Adaptive Filter to Remove Different Noises from ECG Signals. Int. J. Biomed. Eng. Technol. 2020;33:258–273. doi: 10.1504/IJBET.2020.107761. DOI

Kaya I. A Brief Summary of EEG Artifact Handling. arXiv. 2019 doi: 10.13140/RG.2.2.25108.04484.2001.00693 DOI

Jafarifarmand A., Badamchizadeh M.A., Khanmohammadi S., Nazari M.A., Tazehkand B.M. Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach. Biomed. Signal Process. Control. 2017;31:199–210. doi: 10.1016/j.bspc.2016.08.006. DOI

Torse D.A., Desai V.V. Design of Adaptive EEG Preprocessing Algorithm for Neurofeedback System; Proceedings of the 2016 International Conference on Communication and Signal Processing (ICCSP); Melmaruvathur, India. 6–8 April 2016; pp. 392–395. DOI

Rejer I., Górski P. A Multi-Filtering Algorithm for Applying ICA in a Low-Channel EEG. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J.M., editors. Artificial Intelligence and Soft Computing. Volume 11509. Springer International Publishing; Cham, Switzerland: 2019. pp. 283–293. DOI

Dimitriadis S.I., Marimpis A.D. Enhancing performance and bit rates in a brain–computer interface system with phase-to-amplitude cross-frequency coupling: Evidences from traditional c-VEP, Fast c-VEP, and SSVEP designs. Front. Neuroinform. 2018;12:19. doi: 10.3389/fninf.2018.00019. PubMed DOI PMC

Cohen M.X. Assessing transient cross-frequency coupling in EEG data. J. Neurosci. Methods. 2008;168:494–499. doi: 10.1016/j.jneumeth.2007.10.012. PubMed DOI

Knyazev G.G., Savostyanov A.N., Bocharov A.V., Tamozhnikov S.S., Kozlova E.A., Leto I.V., Slobodskaya H.R. Cross-frequency coupling in developmental perspective. Front. Hum. Neurosci. 2019;13:158. doi: 10.3389/fnhum.2019.00158. PubMed DOI PMC

Dimitriadis S.I., Laskaris N.A., Bitzidou M.P., Tarnanas I., Tsolaki M.N. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front. Neurosci. 2015;9:350. doi: 10.3389/fnins.2015.00350. PubMed DOI PMC

Dimitriadis S.I., Laskaris N.A., Simos P.G., Fletcher J.M., Papanicolaou A.C. Greater repertoire and temporal variability of cross-frequency coupling (CFC) modes in resting-state neuromagnetic recordings among children with reading difficulties. Front. Hum. Neurosci. 2016;10:163. doi: 10.3389/fnhum.2016.00163. PubMed DOI PMC

Cohen W.R., Hayes-Gill B. Influence of Maternal Body Mass Index on Accuracy and Reliability of External Fetal Monitoring Techniques. Acta Obstet. et Gynecol. Scand. 2014;93:590–595. doi: 10.1111/aogs.12387. PubMed DOI

Jirsa V., Müller V. Cross-frequency coupling in real and virtual brain networks. Front. Comput. Neurosci. 2013;7:78. doi: 10.3389/fncom.2013.00078. PubMed DOI PMC

Sakkalis V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 2011;41:1110–1117. doi: 10.1016/j.compbiomed.2011.06.020. PubMed DOI

Haufe S., Nikulin V.V., Müller K.R., Nolte G. A critical assessment of connectivity measures for EEG data: A simulation study. Neuroimage. 2013;64:120–133. doi: 10.1016/j.neuroimage.2012.09.036. PubMed DOI

Ullah I., Hussain M., Aboalsamh H. An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 2018;107:61–71. doi: 10.1016/j.eswa.2018.04.021. DOI

Nigam V.P., Graupe D. A neural-network-based detection of epilepsy. Neurol. Res. 2004;26:55–60. doi: 10.1179/016164104773026534. PubMed DOI

Oh S.L., Hagiwara Y., Raghavendra U., Yuvaraj R., Arunkumar N., Murugappan M., Acharya U.R. A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl. 2020;32:10927–10933. doi: 10.1007/s00521-018-3689-5. DOI

Merlin Praveena D., Angelin Sarah D., Thomas George S. Deep learning techniques for EEG signal applications—A review. IETE J. Res. 2020:1–8. doi: 10.1080/03772063.2020.1749143. DOI

Guo L., Rivero D., Dorado J., Rabunal J.R., Pazos A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods. 2010;191:101–109. doi: 10.1016/j.jneumeth.2010.05.020. PubMed DOI

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. 2020;18:031002. doi: 10.1088/1741-2552/abc902. PubMed DOI

Zhang X., Yao L., Zhang S., Kanhere S., Sheng M., Liu Y. Internet of Things meets brain–computer interface: A unified deep learning framework for enabling human-thing cognitive interactivity. IEEE Internet Things J. 2018;6:2084–2092. doi: 10.1109/JIOT.2018.2877786. DOI

Islam M.R., Moni M.A., Islam M.M., Rashed-Al-Mahfuz M., Islam M.S., Hasan M.K., Hossain M.S., Ahmad M., Uddin S., Azad A., et al. Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques. IEEE Access. 2021;9:94601–94624. doi: 10.1109/ACCESS.2021.3091487. DOI

Luo J., Gao X., Zhu X., Wang B., Lu N., Wang J. Motor Imagery EEG Classification Based on Ensemble Support Vector Learning. Comput. Methods Programs Biomed. 2020;193:105464. doi: 10.1016/j.cmpb.2020.105464. PubMed DOI

Sabeti M., Boostani R., Moradi E. Event Related Potential (ERP) as a Reliable Biometric Indicator: A Comparative Approach. Array. 2020;6:100026. doi: 10.1016/j.array.2020.100026. DOI

Markand O.N. Clinical Evoked Potentials. Springer International Publishing; Cham, Switzerland: 2020. Basic Techniques of Evoked Potential Recording; pp. 1–23. DOI

Kumar A., Anand S., Yaddanapudi L.N. Comparison of Auditory Evoked Potential Parameters for Predicting Clinically Anaesthetized State. Acta Anaesthesiol. Scand. 2006;50:1139–1144. doi: 10.1111/j.1399-6576.2006.01137.x. PubMed DOI

Cruccu G., Aminoff M., Curio G., Guerit J., Kakigi R., Mauguiere F., Rossini P., Treede R.D., Garcia-Larrea L. Recommendations for the Clinical Use of Somatosensory-Evoked Potentials. Clin. Neurophysiol. 2008;119:1705–1719. doi: 10.1016/j.clinph.2008.03.016. PubMed DOI

Lueders H., Lesser R.P., Hahn J., Dinner D.S., Klem G. Cortical Somatosensory Evoked Potentials in Response to Hand Stimulation. J. Neurosurg. 1983;58:885–894. doi: 10.3171/jns.1983.58.6.0885. PubMed DOI

Cracco R.Q., Cracco J.B. Somatosensory Evoked Potential in Man: Far Field Potentials. Electroencephalogr. Clin. Neurophysiol. 1976;41:460–466. doi: 10.1016/0013-4694(76)90057-2. PubMed DOI

Chiappa K.H., editor. Evoked Potentials in Clinical Medicine. 3rd ed. Lippincott-Raven; Philadelphia, PA, USA: 1997.

Najarian K. Biomedical Signal and Image Processing. Taylor & Francis; Abingdon, UK: 2016.

Cook M.L., Varela R.A., Goldstein J.D., McCulloch S.D., Bossart G.D., Finneran J.J., Houser D., Mann D.A. Beaked whale auditory evoked potential hearing measurements. J. Comp. Physiol. A. 2006;192:489–495. doi: 10.1007/s00359-005-0086-1. PubMed DOI

Borges L.R., Donadon C., Sanfins M.D., Valente J.P., Paschoal J.R., Colella-Santos M.F. The effects of otitis media with effusion on the measurement of auditory evoked potentials. Int. J. Pediatr. Otorhinolaryngol. 2020;133:109978. doi: 10.1016/j.ijporl.2020.109978. PubMed DOI

Saha S., Mamun K.A., Ahmed K.I.U., Mostafa R., Naik G.R., Darvishi S., Khandoker A.H., Baumert M. Progress in Brain Computer Interface: Challenges and Potentials. Front. Syst. Neurosci. 2021;15:4. doi: 10.3389/fnsys.2021.578875. PubMed DOI PMC

Welschehold S., Boor S., Reuland K., Thömke F., Kerz T., Reuland A., Beyer C., Gartenschläger M., Wagner W., Giese A., et al. Technical Aids in the Diagnosis of Brain Death. Dtsch. Aerzteblatt Online. 2012;109:624. doi: 10.3238/arztebl.2012.0624. PubMed DOI PMC

Picton T.W. Human Auditory Evoked Potentials. Plural Pub; San Diego, CA, USA: 2010.

Capitanio L., Jensen E.W., Filligoi G.C., Makovec B., Gagliardi M., Henneberg S.W., Lindholm P., Cerutti S. On-Line Analysis of AEP and EEG for Monitoring Depth of Anaesthesia. Methods Inf. Med. 1997;36:311–314. doi: 10.1055/s-0038-1636873. PubMed DOI

Arden G.B. Recent Advances in Visual Sciences: The Visual Evoked Response in Ophthalmology. SAGE Publications; Thousand Oaks, CA, USA: 1973.

Cammann R. Use of Visual Evoked Potentials in Neurology—A Review. I. Zentralblatt Fur Neurochir. 1985;46:52. PubMed

Rajbhandari Pandey K., Panday D.R., Limbu N., Shah B., Agarwal K. Effect of Smoking on Visual Evoked Potential (VEP) and Visual Reaction Time (VRT) Asian J. Med Sci. 2020;11:9–13. doi: 10.3126/ajms.v11i2.26689. DOI

Lesiakowski P., Lubiński W., Zwierko T. Analysis of the Relationship Between Training Experience and Visual Sensory Functions in Athletes from Different Sports. Pol. J. Sport Tour. 2017;24:110–114. doi: 10.1515/pjst-2017-0012. DOI

Jung T.P., Makeig S., Westerfield M., Townsend J., Courchesne E., Sejnowski T.J. Analysis and Visualization of Single-Trial Event-Related Potentials. Hum. Brain Mapp. 2001;14:166–185. doi: 10.1002/hbm.1050. PubMed DOI PMC

Handy T.C., editor. Event-Related Potentials: A Methods Handbook. MIT Press; Cambridge, MA, USA: 2005.

Kropotov J.D. Quantitative EEG, Event-Related Potentials and Neurotherapy. 1st ed. Elsevier, Academic Press; Amsterdam, The Netherlands: 2009.

Alvarenga K.F., Amorim R.B., Agostinho-Pesse R.S., Costa O.A., Nascimento L.T., Bevilacqua M.C. Speech Perception and Cortical Auditory Evoked Potentials in Cochlear Implant Users with Auditory Neuropathy Spectrum Disorders. Int. J. Pediatr. Otorhinolaryngol. 2012;76:1332–1338. doi: 10.1016/j.ijporl.2012.06.001. PubMed DOI

Berman S., Backner Y., Krupnik R., Paul F., Petrou P., Karussis D., Levin N., Mezer A.A. Conduction Delays in the Visual Pathways of Progressive Multiple Sclerosis Patients Covary with Brain Structure. NeuroImage. 2020;221:117204. doi: 10.1016/j.neuroimage.2020.117204. PubMed DOI

Abed D.K., Almezel F., Al-Salem Y., Almuhanna D., Algharib N., Aldawood F., Albeladi Q., Kamal A., Abduljabbar O. The Correlation between the Clinical, Radiological and Visual Evoke Potential Findings in Multiple Sclerosis Patients. Bahrain Med. Bull. 2020;42:107–109.

Ford H. Clinical Presentation and Diagnosis of Multiple Sclerosis. Clin. Med. 2020;20:380–383. doi: 10.7861/clinmed.2020-0292. PubMed DOI PMC

Kook H., Gupta L., Kota S., Molfese D., Lyytinen H. An Offline/Real-Time Artifact Rejection Strategy to Improve the Classification of Multi-Channel Evoked Potentials. Pattern Recognit. 2008;41:1985–1996. doi: 10.1016/j.patcog.2007.09.001. DOI

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. doi: 10.1016/j.clinph.2006.10.019. PubMed DOI

Ponton C.W., Don M., Waring M.D., Eggermont J.J., Masuda A. Spatio-Temporal Source Modeling of Evoked Potentials to Acoustic and Cochlear Implant Stimulation. Electroencephalogr. Clin. Neurophysiol. Potentials Sect. 1993;88:478–493. doi: 10.1016/0168-5597(93)90037-P. PubMed DOI

Legatt A.D. Artifacts in Evoked Potential Recordings. In: Tatum W.O., editor. Atlas of Artifacts in Clinical Neurophysiology. Springer Publishing Company; New York, NY, USA: 2018. DOI

Gilley P.M., Sharma A., Dorman M., Finley C.C., Panch A.S., Martin K. Minimization of Cochlear Implant Stimulus Artifact in Cortical Auditory Evoked Potentials. Clin. Neurophysiol. 2006;117:1772–1782. doi: 10.1016/j.clinph.2006.04.018. PubMed DOI

Al-ani T., Cazettes F., Palfi S., Lefaucheur J.P. Automatic Removal of High-Amplitude Stimulus Artefact from Neuronal Signal Recorded in the Subthalamic Nucleus. J. Neurosci. Methods. 2011;198:135–146. doi: 10.1016/j.jneumeth.2011.03.022. PubMed DOI

Beer N.A.M., Velde M., Cluitmans P.J.M. Clinical Evaluation of a Method for Automatic Detection and Removal of Artifacts in Auditory Evoked Potential Monitoring. J. Clin. Monit. 1995;11:381–391. doi: 10.1007/BF01616744. PubMed DOI

Chrapka P. Ph.D. Thesis. McMaster University; Hamilton, ON, Canada: 2018. Advances in EP and ERP Signal Processing.

De Bruin H., Archambeault M., Hasey G. Recording EEG During Repetitive Trans-Cranial Magnetic Stimulation; Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing; Porto, Portugal. 14–17 January 2009; pp. 265–272. DOI

Brodie B.T., Koeman H. Sample and Hold Circuit. 4,302,689. U.S. Patent. 1981 January 26;

Freeman J.A. An Electronic Stimulus Artifact Suppressor. Electroencephalogr. Clin. Neurophysiol. 1971;31:170–172. doi: 10.1016/0013-4694(71)90188-X. PubMed DOI

Roby R.J., Lettich E. A Simplified Circuit for Stimulus Artifact Suppression. Electroencephalogr. Clin. Neurophysiol. 1975;39:85–87. doi: 10.1016/0013-4694(75)90130-3. PubMed DOI

Babb T.L., Mariani E., Strain G.M., Lieb J.P., Soper H.V., Crandall P.H. A Sample and Hold Amplifier System for Stimulus Artifact Suppression. Electroencephalogr. Clin. Neurophysiol. 1978;44:528–531. doi: 10.1016/0013-4694(78)90038-X. PubMed DOI

Peper A., Grimbergen C.A. EEG Measurement During Electrical Stimulation. IEEE Trans. Biomed. Eng. 1983;BME-30:231–233. doi: 10.1109/TBME.1983.325224. PubMed DOI

Heffer L.F., Fallon J.B. A Novel Stimulus Artifact Removal Technique for High-Rate Electrical Stimulation. J. Neurosci. Methods. 2008;170:277–284. doi: 10.1016/j.jneumeth.2008.01.023. PubMed DOI PMC

Schoenecker M.C., Bonham B.H. Fast Stimulus Artifact Recovery in a Multichannel Neural Recording System; Proceedings of the 2008 IEEE Biomedical Circuits and Systems Conference; Baltimore, MD, USA. 20–22 November 2008; pp. 253–256. PubMed DOI PMC

Chiappa K.H., Ropper A.H. Evoked Potentials in Clinical Medicine. N. Engl. J. Med. 1982;306:1140–1150. doi: 10.1056/NEJM198205133061904. PubMed DOI

Cao T., Wan F., Wong C., da Cruz J., Hu Y. Objective Evaluation of Fatigue by EEG Spectral Analysis in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces. BioMed. Eng. OnLine. 2014;13:28. doi: 10.1186/1475-925X-13-28. PubMed DOI PMC

Somers B., Francart T., Bertrand A. A Generic EEG Artifact Removal Algorithm Based on the Multi-Channel Wiener Filter. J. Neural Eng. 2018;15:036007. doi: 10.1088/1741-2552/aaac92. PubMed DOI

Wang T., Özdamar Ö., Bohórquez J., Shen Q., Cheour M. Wiener Filter Deconvolution of Overlapping Evoked Potentials. J. Neurosci. Methods. 2006;158:260–270. doi: 10.1016/j.jneumeth.2006.05.023. PubMed DOI

Cichocki A., Gharieb R., Hoya T. Efficient Extraction of Evoked Potentials by Combination of Wiener Filtering and Subspace Methods; Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No.01CH37221); Salt Lake City, UT, USA. 7–11 May 2001; pp. 3117–3120. DOI

Paul J., Luft A., Hanley D., Thakor N. Coherence-Weighted Wiener Filtering of Somatosensory Evoked Potentials. IEEE Trans. Biomed. Eng. 2001;48:1483–1488. doi: 10.1109/10.966608. PubMed DOI

Lin B.S., Lin B.S., Chong F.C., Lai F. Adaptive Filtering of Evoked Potentials Using Higher-Order Adaptive Signal Enhancer with Genetic-Type Variable Step-Size Prefilter. Med. Biol. Eng. Comput. 2005;43:638–647. doi: 10.1007/BF02351038. PubMed DOI

Ahirwal M.K., Kumar A., Singh G.K. Adaptive Filtering of EEG/ERP through Noise Cancellers Using an Improved PSO Algorithm. Swarm Evol. Comput. 2014;14:76–91. doi: 10.1016/j.swevo.2013.10.001. DOI

Dien J. Addressing Misallocation of Variance in Principal Components Analysis of Event-Related Potentials. Brain Topogr. 1998;11:43–55. doi: 10.1023/A:1022218503558. PubMed DOI

Quian Quiroga R. Obtaining Single Stimulus Evoked Potentials with Wavelet Denoising. Phys. D Nonlinear Phenom. 2000;145:278–292. doi: 10.1016/S0167-2789(00)00116-0. DOI

Quian Quiroga R., van Luijtelaar E. Habituation and Sensitization in Rat Auditory Evoked Potentials: A Single-Trial Analysis with Wavelet Denoising. Int. J. Psychophysiol. 2002;43:141–153. doi: 10.1016/S0167-8760(01)00157-X. PubMed DOI

Quiroga R., Garcia H. Single-Trial Event-Related Potentials with Wavelet Denoising. Clin. Neurophysiol. 2003;114:376–390. doi: 10.1016/S1388-2457(02)00365-6. PubMed DOI

Ahmadi M., Quian Quiroga R. Automatic Denoising of Single-Trial Evoked Potentials. NeuroImage. 2013;66:672–680. doi: 10.1016/j.neuroimage.2012.10.062. PubMed DOI

Shapiro J.M. Embedded Image Coding Using Zerotrees of Wavelet Coefficients. In: Topiwala P.N., editor. Wavelet Image and Video Compression. Volume 450. Kluwer Academic Publishers; Boston, MA, USA: 2002. pp. 123–155. DOI

Wang Z., Maier A., Leopold D.A., Logothetis N.K., Liang H. Single-Trial Evoked Potential Estimation Using Wavelets. Comput. Biol. Med. 2007;37:463–473. doi: 10.1016/j.compbiomed.2006.08.011. PubMed DOI

Iyer D., Zouridakis G. Single-Trial Evoked Potential Estimation: Comparison between Independent Component Analysis and Wavelet Denoising. Clin. Neurophysiol. 2007;118:495–504. doi: 10.1016/j.clinph.2006.10.024. PubMed DOI

Zouridakis G., Iyer D. Comparison between ICA and Wavelet-Based Denoising of Single-Trial Evoked Potentials; Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; San Francisco, CA, USA. 1–5 September 2004; pp. 87–90. PubMed DOI

Lee P.L., Hsieh J.C., Wu C.H., Shyu K.K., Chen S.S., Yeh T.C., Wu Y.T. The Brain Computer Interface Using Flash Visual Evoked Potential and Independent Component Analysis. Ann. Biomed. Eng. 2006;34:1641–1654. doi: 10.1007/s10439-006-9175-8. PubMed DOI

Patidar U., Zouridakis G. A Hybrid Algorithm for Artifact Rejection in EEG Recordings Based on Iterative ICA and Fuzzy Clustering; Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Vancouver, BC, Canada. 20–25 August 2008; pp. 50–53. PubMed DOI

Palaniappan R., Ravi K. Improving Visual Evoked Potential Feature Classification for Person Recognition Using PCA and Normalization. Pattern Recognit. Lett. 2006;27:726–733. doi: 10.1016/j.patrec.2005.10.020. DOI

Palaniappan R., Anandan S., Raveendran P. Two Level PCA to Reduce Noise and EEG from Evoked Potential Signals; Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision; Singapore. 2–5 December 2002; pp. 1688–1693. DOI

Mowla M.R., Ng S.C., Zilany M.S.A., Paramesran R. Single-Trial Evoked Potential Estimation Using Iterative Principal Component Analysis. IEEE Sens. J. 2016;16:6955–6960. doi: 10.1109/JSEN.2016.2591582. DOI

Hu L., Mouraux A., Hu Y., Iannetti G. A Novel Approach for Enhancing the Signal-to-Noise Ratio and Detecting Automatically Event-Related Potentials (ERPs) in Single Trials. NeuroImage. 2010;50:99–111. doi: 10.1016/j.neuroimage.2009.12.010. PubMed DOI

Zou L., Zhang Y., Yang L.T., Zhou R. Single-Trial Evoked Potentials Study by Combining Wavelet Denoising and Principal Component Analysis Methods. J. Clin. Neurophysiol. 2010;27:17–24. doi: 10.1097/WNP.0b013e3181c9b29a. PubMed DOI

Wang D.D., Chen W., Starr P.A., de Hemptinne C. Local Field Potentials and ECoG. In: Pouratian N., Sheth S.A., editors. Stereotactic and Functional Neurosurgery. Springer International Publishing; Cham, Switzerland: 2020. pp. 107–117. DOI

Nakasatp N., Levesque M.F., Barth D.S., Baumgartner C., Rogers R.L., Sutherling W.W. Comparisons of MEG, EEG, and ECoG Source Localization in Neocortical Partial Epilepsy in Humans. Electroencephalogr. Clin. Neurophysiol. 1994;91:171–178. doi: 10.1016/0013-4694(94)90067-1. PubMed DOI

RaviPrakash H., Korostenskaja M., Castillo E.M., Lee K.H., Salinas C.M., Baumgartner J., Anwar S.M., Spampinato C., Bagci U. Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery. Front. Neurosci. 2020;14:409. doi: 10.3389/fnins.2020.00409. PubMed DOI PMC

Hashiguchi K., Morioka T., Yoshida F., Miyagi Y., Nagata S., Sakata A., Sasaki T. Correlation between Scalp-Recorded Electroencephalographic and Electrocorticographic Activities during Ictal Period. Seizure. 2007;16:238–247. doi: 10.1016/j.seizure.2006.12.010. PubMed DOI

Asano E., Juhasz C., Shah A., Muzik O., Chugani D.C., Shah J., Sood S., Chugani H.T. Origin and Propagation of Epileptic Spasms Delineated on Electrocorticography. Epilepsia. 2005;46:1086–1097. doi: 10.1111/j.1528-1167.2005.05205.x. PubMed DOI PMC

Logothetis N.K. The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal. J. Neurosci. 2003;23:3963–3971. doi: 10.1523/JNEUROSCI.23-10-03963.2003. PubMed DOI PMC

Ulbert I., Halgren E., Heit G., Karmos G. Multiple Microelectrode-Recording System for Human Intracortical Applications. J. Neurosci. Methods. 2001;106:69–79. doi: 10.1016/S0165-0270(01)00330-2. PubMed DOI

van’t Klooster M.A., van Klink N.E., Zweiphenning W.J., Leijten F.S., Zelmann R., Ferrier C.H., van Rijen P.C., Otte W.M., Braun K.P., Huiskamp G.J., et al. Tailoring Epilepsy Surgery with Fast Ripples in the Intraoperative Electrocorticogram: Tailoring Epilepsy Surgery With Fast Ripples. Ann. Neurol. 2017;81:664–676. doi: 10.1002/ana.24928. PubMed DOI

Sellers K.K., Schuerman W.L., Dawes H.E., Chang E.F., Leonard M.K. Comparison of Common Artifact Rejection Methods Applied to Direct Cortical and Peripheral Stimulation in Human ECoG; Proceedings of the 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER); San Francisco, CA, USA. 20–23 March 2019; pp. 77–80. DOI

Caldwell D.J., Cronin J.A., Rao R.P.N., Collins K.L., Weaver K.E., Ko A.L., Ojemann J.G., Kutz J.N., Brunton B.W. Signal Recovery from Stimulation Artifacts in Intracranial Recordings with Dictionary Learning. J. Neural Eng. 2020;17:026023. doi: 10.1088/1741-2552/ab7a4f. PubMed DOI PMC

Schweigmann M., Koch K.P., Auler F., Kirchhoff F. Improving Electrocorticograms of Awake and Anaesthetized Mice Using Wavelet Denoising. Curr. Dir. Biomed. Eng. 2018;4:469–472. doi: 10.1515/cdbme-2018-0112. DOI

Freeman W.J., Rogers L.J., Holmes M.D., Silbergeld D.L. Spatial Spectral Analysis of Human Electrocorticograms Including the Alpha and Gamma Bands. J. Neurosci. Methods. 2000;95:111–121. doi: 10.1016/S0165-0270(99)00160-0. PubMed DOI

Deeb S.E. Ph.D. Thesis. San Diego State University; San Diego, CA, USA: 2019. Analysis of Globus Pallidus Local Field Potentials and Electrocorticograms of Patients Diagnosed with Parkinson’s Disease—ProQuest.

Chen Z., Huang L., Shen Y., Wang J., Zhao R., Dai J. A New Algorithm for Classification of Ictal and Pre-Ictal Epilepsy ECoG Using MI and SVM; Proceedings of the 2017 International Conference on Signals and Systems (ICSigSys); Bali, Indonesia. 16–18 May 2017; pp. 212–216. DOI

Hossain G., Myers M.H., Kozma R. Study of Phase Relationships in ECoG Signals Using Hilbert-Huang Transforms. In: Hutchison D., Kanade T., Kittler J., Kleinberg J.M., Mattern F., Mitchell J.C., Naor M., Nierstrasz O., Pandu Rangan C., Steffen B., et al., editors. Advances in Brain Inspired Cognitive Systems. Volume 7366. Springer; Berlin/Heidelberg, Germany: 2012. pp. 174–182. DOI

Seo J.H., Tsuda I., Lee Y.J., Ikeda A., Matsuhashi M., Matsumoto R., Kikuchi T., Kang H. Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition. Mathematics. 2020;8:481. doi: 10.3390/math8040481. DOI

Ince N.F., Goksu F., Tewfik A.H. An ECoG Based Brain Computer Interface with Spatially Adapted Time-Frequency Patterns; Proceedings of the 2008 International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2008); Funchal, Madeira–Portugal. 28–31 January 2008; pp. 132–139.

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

Bartolomei F., Lagarde S., Wendling F., McGonigal A., Jirsa V., Guye M., Bénar C. Defining epileptogenic networks: Contribution of SEEG and signal analysis. Epilepsia. 2017;58:1131–1147. doi: 10.1111/epi.13791. PubMed DOI

Mullin J.P., Shriver M., Alomar S., Najm I., Bulacio J., Chauvel P., Gonzalez-Martinez J. Is SEEG safe? A systematic review and meta-analysis of stereo-electroencephalography—Related complications. Epilepsia. 2016;57:386–401. doi: 10.1111/epi.13298. PubMed DOI

Gordon E., Konopka L.M. EEG databases in research and clinical practice: Current status and future directions. Clin. EEG Neurosci. 2005;36:53. doi: 10.1177/155005940503600203. PubMed DOI

Agarwal M. EEG Databases—GitHub Repository. 2021. [(accessed on 14 June 2021)]. Available online: https://github.com/meagmohit/EEG-Datasets.

Van Horn J.D., Grafton S.T., Rockmore D., Gazzaniga M.S. Sharing neuroimaging studies of human cognition. Nat. Neurosci. 2004;7:473–481. doi: 10.1038/nn1231. PubMed DOI

Physionet—Data Base. 2021. [(accessed on 12 April 2021)]. Available online: https://physionet.org.

Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.C., Mark R.G., Mietus J.E., Moody G.B., Peng C.K., Stanley H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000;101:e215–e220. doi: 10.1161/01.CIR.101.23.e215. PubMed DOI

Sweeney K.T., Ayaz H., Ward T.E., Izzetoglu M., McLoone S.F., Onaral B. A methodology for validating artifact removal techniques for physiological signals. IEEE Trans. Inf. Technol. Biomed. 2012;16:918–926. doi: 10.1109/TITB.2012.2207400. PubMed DOI

Kemp B., Zwinderman A.H., Tuk B., Kamphuisen H.A., Oberye J.J. Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 2000;47:1185–1194. doi: 10.1109/10.867928. PubMed DOI

Abel J.H., Badgeley M.A., Meschede-Krasa B., Schamberg G., Garwood I.C., Lecamwasam K., Chakravarty S., Zhou D.W., Keating M., Purdon P.L., et al. Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia. PLoS ONE. 2021;16:e0246165. doi: 10.1371/journal.pone.0246165. PubMed DOI PMC

Detti P., Vatti G., Zabalo Manrique de Lara G. EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings. Processes. 2020;8:846. doi: 10.3390/pr8070846. DOI

Detti P. Siena Scalp EEG Database (Version 1.0.0) PhysioNet. 2020 doi: 10.13026/5d4a-j060. DOI

Shoeb A.H. Ph.D. Thesis. Massachusetts Institute of Technology; Cambridge, MA, USA: 2009. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment.

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

Zyma I., Tukaev S., Seleznov I., Kiyono K., Popov A., Chernykh M., Shpenkov O. Electroencephalograms during mental arithmetic task performance. Data. 2019;4:14. doi: 10.3390/data4010014. DOI

Matran-Fernandez A., Poli R. Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces. PLoS ONE. 2017;12:e0178498. doi: 10.1371/journal.pone.0178498. PubMed DOI PMC

Oikonomou V.P., Liaros G., Georgiadis K., Chatzilari E., Adam K., Nikolopoulos S., Kompatsiaris I. Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv. 20161602.00904

Hu L., Zhang Z. EEG Signal Processing and Feature Extraction. Springer; Berlin, Germany: 2019.

Chen S., He Z., Han X., He X., Li R., Zhu H., Zhao D., Dai C., Zhang Y., Lu Z., et al. How big data and high-performance computing drive brain science. Genom. Proteom. Bioinform. 2019;17:381–392. doi: 10.1016/j.gpb.2019.09.003. PubMed DOI PMC

Landhuis E. Neuroscience: Big brain, big data. Nature. 2017;541:559–561. doi: 10.1038/541559a. PubMed DOI

Cavanagh J.F. Electrophysiology as a theoretical and methodological hub for the neural sciences. Psychophysiology. 2019;56:e13314. doi: 10.1111/psyp.13314. PubMed DOI PMC

Khosla A., Khandnor P., Chand T. A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern. Biomed. Eng. 2020;40:649–690. doi: 10.1016/j.bbe.2020.02.002. DOI

Blankertz B., Lemm S., Treder M., Haufe S., Müller K.R. Single-trial analysis and classification of ERP components—A tutorial. NeuroImage. 2011;56:814–825. doi: 10.1016/j.neuroimage.2010.06.048. PubMed DOI

Makeig S., Kothe C., Mullen T., Bigdely-Shamlo N., Zhang Z., Kreutz-Delgado K. Evolving signal processing for brain–computer interfaces. Proc. IEEE. 2012;100:1567–1584. doi: 10.1109/JPROC.2012.2185009. DOI

Müller K.R., Tangermann M., Dornhege G., Krauledat M., Curio G., Blankertz B. Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring. J. Neurosci. Methods. 2008;167:82–90. doi: 10.1016/j.jneumeth.2007.09.022. PubMed DOI

Neuner I., Arrubla J., Werner C.J., Hitz K., Boers F., Kawohl W., Shah N.J. The default mode network and EEG regional spectral power: A simultaneous fMRI-EEG study. PLoS ONE. 2014;9:e88214. doi: 10.1371/journal.pone.0088214. PubMed DOI PMC

Ritter P., Villringer A. simultaneous EEG–fMRI. Neurosci. Biobehav. Rev. 2006;30:823–838. doi: 10.1016/j.neubiorev.2006.06.008. PubMed DOI

Huster R.J., Debener S., Eichele T., Herrmann C.S. Methods for simultaneous EEG-fMRI: An introductory review. J. Neurosci. 2012;32:6053–6060. doi: 10.1523/JNEUROSCI.0447-12.2012. PubMed DOI PMC

Mishra V., Gautier N.M., Glasscock E. Simultaneous Video-EEG-ECG monitoring to identify neurocardiac dysfunction in mouse models of epilepsy. J. Vis. Exp. Jove. 2018:57300. doi: 10.3791/57300. PubMed DOI PMC

Niegowski M., Zivanovic M. ECG-EMG separation by using enhanced non-negative matrix factorization; Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Chicago, IL, USA. 26–30 August 2014; Piscataway, NJ, USA: IEEE; 2014. pp. 4212–4215. PubMed

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; Piscataway, NJ, USA: IEEE; 2017. pp. 642–645.

Yang J., Cha S., Yun D., An J. Probe Configuration Design for Closed-loop Multi-Channel fNIRS-tDCS BCI; Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI); Gangwon, Korea. 26–28 February 2020; Piscataway, NJ, USA: IEEE; 2020. pp. 1–3.

Matarasso A.K., Rieke J.D., White K., Yusufali M.M., Daly J.J. Combined real-time fMRI and real time fNIRS brain computer interface (BCI): Training of volitional wrist extension after stroke, a case series pilot study. PLoS ONE. 2021;16:e0250431. doi: 10.1371/journal.pone.0250431. PubMed DOI PMC

Val-Calvo M., Álvarez-Sánchez J.R., Ferrández-Vicente J.M., Díaz-Morcillo A., Fernández-Jover E. Real-time multi-modal estimation of dynamically evoked emotions using EEG, heart rate and galvanic skin response. Int. J. Neural Syst. 2020;30:2050013. doi: 10.1142/S0129065720500136. PubMed DOI

Park H.J., Han J.M., Jeong D.U., Park K.S. A study on the elimination of the ECG artifact in the polysomnographic EEG and EOG using AR model; Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No. 98CH36286); Hong Kong, China. 1 November 1998; Piscataway, NJ, USA: IEEE; 1998. pp. 1632–1635.

Sanei S., Chambers J.A. EEG Signal Processing. John Wiley & Sons; Hoboken, NJ, USA: 2013.

Yan W.X., Mullinger K.J., Brookes M.J., Bowtell R. Understanding gradient artefacts in simultaneous EEG/fMRI. Neuroimage. 2009;46:459–471. doi: 10.1016/j.neuroimage.2009.01.029. PubMed DOI

Guarnieri R. Ph.D. Thesis. KU Leuven; Leuven, Belgium: Jun, 2021. Applications.

Rogasch N.C., Thomson R.H., Farzan F., Fitzgibbon B.M., Bailey N.W., Hernandez-Pavon J.C., Daskalakis Z.J., Fitzgerald P.B. Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties. Neuroimage. 2014;101:425–439. doi: 10.1016/j.neuroimage.2014.07.037. PubMed DOI

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

Bhattacharyya S., Das S., Das A., Dey R., Dhar R. Neuro-feedback system for real-time BCI decision prediction. Microsyst. Technol. 2021;27:3725–3734. doi: 10.1007/s00542-020-05146-4. DOI

Martišius I., Damaševičius R. A prototype SSVEP based real time BCI gaming system. Comput. Intell. Neurosci. 2016;2016:3861425. doi: 10.1155/2016/3861425. PubMed DOI PMC

Martinek R., Ladrova M., Sidikova M., Jaros R., Behbehani K., Kahankova R., Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals. Sensors. 2021;21:5186. doi: 10.3390/s21155186. PubMed DOI PMC

Alipour A., Rezai A., Hashemi T., Yousefpour N. The effectiveness of cognitive behavioral therapy focused on lifestyle modification to increase monitoring vital signs and coronary heart disease and psychological well-being. Q. J. Health Psychol. 2017;5:125–136.

Fioranelli F., Le Kernec J., Shah S.A. Radar for health care: Recognizing human activities and monitoring vital signs. IEEE Potentials. 2019;38:16–23. doi: 10.1109/MPOT.2019.2906977. DOI

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

Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers