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Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.<\/jats:p>","DOI":"10.3390\/s23146434","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T01:06:36Z","timestamp":1689555996000},"page":"6434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":229,"title":["Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3402-9576","authenticated-orcid":false,"given":"Ahmad","family":"Chaddad","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Sup\u00e9rieure, Montreal, QC H3C 1K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8640-8650","authenticated-orcid":false,"given":"Yihang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Reem","family":"Kateb","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Madinah 41477, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1474-2772","authenticated-orcid":false,"given":"Ahmed","family":"Bouridane","sequence":"additional","affiliation":[{"name":"Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.eng.2019.11.012","article-title":"From brain science to artificial intelligence","volume":"6","author":"Fan","year":"2020","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1017\/S0140525X14000041","article-title":"Memory reconsolidation, emotional arousal, and the process of change in psychotherapy: New insights from brain science","volume":"38","author":"Lane","year":"2015","journal-title":"Behav. Brain Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1016\/j.neuron.2013.10.017","article-title":"EEG and MEG: Relevance to neuroscience","volume":"80","year":"2013","journal-title":"Neuron"},{"key":"ref_4","unstructured":"Da Silva, F.L. (2023). EEG-fMRI: Physiological Basis, Technique, and Applications, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Saibene, A., Caglioni, M., Corchs, S., and Gasparini, F. (2023). EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. Sensors, 23.","DOI":"10.20944\/preprints202302.0096.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1151852","DOI":"10.3389\/fncom.2023.1151852","article-title":"Recent advances in EEG (non-invasive) based BCI applications","volume":"17","author":"Islam","year":"2023","journal-title":"Front. Comput. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111591","DOI":"10.1016\/j.pscychresns.2023.111591","article-title":"Review of EEG-based neurofeedback as a therapeutic intervention to treat depression","volume":"329","author":"Patil","year":"2023","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mulert, C., and Lemieux, L. (2023). EEG-fMRI: Physiological Basis, Technique, and Applications, Springer.","DOI":"10.1007\/978-3-031-07121-8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1007\/s00415-022-11047-5","article-title":"Cognitive, EEG, and MRI features of COVID-19 survivors: A 10-month study","volume":"269","author":"Cecchetti","year":"2022","journal-title":"J. Neurol."},{"key":"ref_10","first-page":"43965","article-title":"Brain function diagnosis enhanced using denoised fNIRS raw signals","volume":"2014","author":"Chaddad","year":"2014","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Kamrani, E., Le Lan, J., and Sawan, M. (2013, January 3\u20135). Denoising fNIRS signals to enhance brain imaging diagnosis. Proceedings of the 2013 29th Southern Biomedical Engineering Conference, Miami, FL, USA.","DOI":"10.1109\/SBEC.2013.25"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chaddad, A. (2014, January 19\u201321). Brain function evaluation using enhanced fNIRS signals extraction. Proceedings of the 2014 48th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.","DOI":"10.1109\/CISS.2014.6814079"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1007\/s00415-023-11619-z","article-title":"Neurological update: Structural and functional imaging in epilepsy surgery","volume":"270","author":"Yoganathan","year":"2023","journal-title":"J. Neurol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12847","DOI":"10.3390\/s140712847","article-title":"Dry EEG electrodes","volume":"14","author":"Valle","year":"2014","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Orban, M., Elsamanty, M., Guo, K., Zhang, S., and Yang, H. (2022). A Review of Brain Activity and EEG-Based Brain\u2013Computer Interfaces for Rehabilitation Application. Bioengineering, 9.","DOI":"10.3390\/bioengineering9120768"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.2302\/kjm.2022-0002-OA","article-title":"Brain\u2013machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis","volume":"71","author":"Liu","year":"2022","journal-title":"Keio J. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.3233\/JAD-220508","article-title":"Systematic Review of EEG Coherence in Alzheimer\u2019s Disease","volume":"91","author":"Fischer","year":"2023","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_18","first-page":"1","article-title":"Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer\u2019s disease using EEG technology","volume":"15","author":"Jiao","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e2225098","DOI":"10.1001\/jamanetworkopen.2022.25098","article-title":"Analysis of clinical features, diagnostic tests, and biomarkers in patients with suspected Creutzfeldt-Jakob disease, 2014\u20132021","volume":"5","author":"Shir","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102569","DOI":"10.1016\/j.nicl.2021.102569","article-title":"EEG measures of sensorimotor processing and their development are abnormal in children with isolated dystonia and dystonic cerebral palsy","volume":"30","author":"McClelland","year":"2021","journal-title":"NeuroImage Clin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TETCI.2022.3186180","article-title":"A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals","volume":"7","author":"Alvi","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1146\/annurev.bioeng.5.040202.121601","article-title":"Advances in quantitative electroencephalogram analysis methods","volume":"6","author":"Thakor","year":"2004","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1007\/s11831-021-09684-6","article-title":"Review of machine learning techniques for EEG based brain computer interface","volume":"29","author":"Aggarwal","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10916-008-9231-z","article-title":"EEG signal analysis: A survey","volume":"34","author":"Subha","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gaddipati, B., Nelakuditi, U.R., and Medithe, J.W.C. (2016, January 26\u201327). Single lead EEG acquisition system for health care applications. Proceedings of the 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.","DOI":"10.1109\/INVENTIVE.2016.7823238"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Choi, H., Park, J., and Yang, Y.M. (2022). A Novel Quick-Response Eigenface Analysis Scheme for Brain\u2013Computer Interfaces. Sensors, 22.","DOI":"10.3390\/s22155860"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1136\/jnnp.74.1.9","article-title":"Hans berger (1873\u20131941), richard caton (1842\u20131926), and electroencephalography","volume":"74","author":"Haas","year":"2003","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_29","unstructured":"Stytsenko, K., Jablonskis, E., and Prahm, C. (2011, January 21\u201323). Evaluation of consumer EEG device Emotiv EPOC. Proceedings of the MEi: CogSci Conference, Ljubljana, Slovenia."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Holewa, K., and Nawrocka, A. (2014, January 28\u201330). Emotiv EPOC neuroheadset in brain\u2013computer interface. Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), Velke Karlovice, Czech Republic.","DOI":"10.1109\/CarpathianCC.2014.6843587"},{"key":"ref_31","first-page":"2012","article-title":"A P300-based quantitative comparison between the Emotiv Epoc headset and a medical EEG device","volume":"765","author":"Duvinage","year":"2012","journal-title":"Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/19.746581","article-title":"A CMOS IC for portable EEG acquisition systems","volume":"47","author":"Martins","year":"1998","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cui, Y., Tian, F., Zhao, Q., and Hu, B. (2021, January 9\u201312). Design and Application of a Portable Sleep Inertia Detection System Based on EEG Signals. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669142"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, Y., Li, N., Li, Y., and Liu, W. (2021). A portable waterproof EEG acquisition device for dolphins. Sensors, 21.","DOI":"10.3390\/s21103336"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neucli.2019.12.004","article-title":"Stroke identification using a portable EEG device\u2013A pilot study","volume":"50","author":"Gottlibe","year":"2020","journal-title":"Neurophysiol. Clin."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, X., Mi, X., and Pan, S. (2014, January 14\u201316). A high precision EEG acquisition system based on the CompactPCI platform. Proceedings of the 2014 7th International Conference on Biomedical Engineering and Informatics, Dalian, China.","DOI":"10.1109\/BMEI.2014.7002828"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1523\/JNEUROSCI.6135-10.2011","article-title":"Deep brain stimulation of the subthalamic nucleus alters the cortical profile of response inhibition in the beta frequency band: A scalp EEG study in Parkinson\u2019s disease","volume":"31","author":"Swann","year":"2011","journal-title":"J. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"012045","DOI":"10.1088\/1742-6596\/1907\/1\/012045","article-title":"A review of EEG acquisition, processing and application","volume":"1907","author":"Li","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_39","unstructured":"Bajaj, N., Carri\u00f3n, J.R., and Bellotti, F. (2020). Phyaat: Physiology of auditory attention to speech dataset. arXiv."},{"key":"ref_40","unstructured":"Luck, S.J. (2014). An Introduction to the Event-Related Potential Technique, MIT Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cogr.2021.04.001","article-title":"Review of the emotional feature extraction and classification using EEG signals","volume":"1","author":"Wang","year":"2021","journal-title":"Cogn. Robot."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, C., Zhang, M., Sun, X., Wang, H., and Gao, Z. (2023). Dynamic threshold distribution domain adaptation network: A cross-subject fatigue recognition method based on EEG signals. IEEE Trans. Cogn. Dev. Syst., early access.","DOI":"10.1109\/TCDS.2023.3257428"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gamage, T.A., Kalansooriya, L.P., and Sandamali, E.R.C. (2022, January 1). An Emotion Classification Model for Driver Emotion Recognition Using Electroencephalography (EEG). Proceedings of the 2022 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka.","DOI":"10.1109\/SCSE56529.2022.9905108"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TNSRE.2022.3221962","article-title":"Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition","volume":"31","author":"Shen","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"103985","DOI":"10.1016\/j.autcon.2021.103985","article-title":"Applications of electroencephalography in construction","volume":"133","author":"Saedi","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116443","DOI":"10.1016\/j.eswa.2021.116443","article-title":"Deep Convolutional Neural Network Based Eye States Classification Using Ear-EEG","volume":"192","author":"Han","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pawu\u015b, D., and Paszkiel, S. (2022). The application of integration of EEG signals for authorial classification algorithms in implementation for a mobile robot control using movement imagery\u2014Pilot study. Appl. Sci., 12.","DOI":"10.3390\/app12042161"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/MSP.2021.3134629","article-title":"Toward open-world electroencephalogram decoding via deep learning: A comprehensive survey","volume":"39","author":"Chen","year":"2022","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TNSRE.2022.3161989","article-title":"A pre-gelled EEG electrode and its application in SSVEP-based BCI","volume":"30","author":"Pei","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"60141","DOI":"10.1109\/ACCESS.2022.3176367","article-title":"An interpretable deep learning classifier for epileptic seizure prediction using EEG data","volume":"10","author":"Jemal","year":"2022","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neunet.2021.12.010","article-title":"Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation","volume":"148","author":"Wen","year":"2022","journal-title":"Neural Netw."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"10751","DOI":"10.1109\/JSEN.2022.3168572","article-title":"An EEG data processing approach for emotion recognition","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Freismuth, D., and TaheriNejad, N. (2022). On the treatment and diagnosis of attention deficit hyperactivity disorder with eeg assistance. Electronics, 11.","DOI":"10.3390\/electronics11040606"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jiang, X., Bian, G.B., and Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors, 19.","DOI":"10.3390\/s19050987"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1504\/IJBET.2015.071012","article-title":"Methods of denoising of electroencephalogram signal: A review","volume":"18","author":"Sheoran","year":"2015","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1111\/j.1469-8986.1991.tb03397.x","article-title":"Removal of the ocular artifact from the EEG: A comparison of time and frequency domain methods with simulated and real data","volume":"28","author":"Kenemans","year":"1991","journal-title":"Psychophysiology"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0987-7053(00)00055-1","article-title":"Removal of ocular artifact from the EEG: A review","volume":"30","author":"Croft","year":"2000","journal-title":"Neurophysiol. Clin. Neurophysiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.bbe.2021.06.007","article-title":"Ocular artifact elimination from electroencephalography signals: A systematic review","volume":"41","author":"Ranjan","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TNSRE.2020.2986099","article-title":"Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control","volume":"28","author":"Stachaczyk","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1049\/ell2.12519","article-title":"Systematic evaluation of recursive approach of EEG-segment-based PCA for removal of helium-pump artefact from MRI","volume":"58","author":"Kim","year":"2022","journal-title":"Electron. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"102168","DOI":"10.1016\/j.bspc.2020.102168","article-title":"Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG\u2014An efficient approach combining SSA\u2013ICA with wavelet thresholding for BCI applications","volume":"63","author":"Noorbasha","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103292","DOI":"10.1016\/j.bspc.2021.103292","article-title":"Which BSS method separates better the EEG Signals? A comparison of five different algorithms","volume":"72","author":"Stergiadis","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1109\/TBME.2010.2051440","article-title":"Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis","volume":"57","author":"Taelman","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.neubiorev.2006.06.007","article-title":"Imaging human EEG dynamics using independent component analysis","volume":"30","author":"Onton","year":"2006","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"597941","DOI":"10.3389\/fnins.2020.597941","article-title":"Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)\u2014A novel ICA-based algorithm for removing myoelectric artifacts from EEG","volume":"14","author":"Li","year":"2021","journal-title":"Front. Neurosci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1109\/TNSRE.2022.3176575","article-title":"ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG","volume":"30","author":"Maddirala","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compeleceng.2015.08.019","article-title":"Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis","volume":"54","author":"Patel","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Xie, Z., Yu, B., and Xu, W. (2021, January 4\u20136). A Learning Model of Evoked EEG Signals Based on PCA and Semi-supervised SVM. Proceedings of the 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE), Wuhan, China.","DOI":"10.1109\/RCAE53607.2021.9638947"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.procs.2020.03.386","article-title":"A new method for automatic electrooculogram and eye blink artifacts correction of EEG signals using CCA and NAPCT","volume":"167","author":"Sheoran","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.dsp.2014.06.006","article-title":"Detrended fluctuation thresholding for empirical mode decomposition based denoising","volume":"32","author":"Mert","year":"2014","journal-title":"Digit. Signal Process."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TBME.2006.879459","article-title":"Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram","volume":"53","author":"Vergult","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1109\/TBME.2011.2151861","article-title":"Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram","volume":"58","author":"Hassan","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1109\/JBHI.2021.3131186","article-title":"Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG","volume":"26","author":"Miao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_74","first-page":"1","article-title":"Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model","volume":"71","author":"Ranjan","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3183","DOI":"10.1109\/TBME.2022.3162490","article-title":"Removal of Transcranial Alternating Current Stimulation EEG Artifacts Using Blind Source Separation and Wavelets","volume":"69","author":"Yan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.bspc.2015.06.009","article-title":"Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising","volume":"22","author":"Mowla","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Dautov, C.P., and Ozerdem, M.S. (2018, January 2\u20135). Wavelet transform and signal denoising using Wavelet method. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404418"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2016.2544298","article-title":"Comparative study of wavelet-based unsupervised ocular artifact removal techniques for single-channel EEG data","volume":"4","author":"Khatun","year":"2016","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3991\/ijoe.v13i09.7159","article-title":"ECG Signal Denoising by Discrete Wavelet Transform","volume":"13","author":"Aqil","year":"2017","journal-title":"Int. J. Online Eng."},{"key":"ref_80","unstructured":"Zhou, W., and Gotman, J. (2004, January 1\u20135). Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA. Proceedings of the The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA."},{"key":"ref_81","unstructured":"Tibdewal, M.N., Mahadevappa, M., Ray, A.K., Malokar, M., and Dey, H.R. (2016, January 16\u201318). Power line and ocular artifact denoising from EEG using notch filter and wavelet transform. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Zhang, D.X., Wu, X.P., and Guo, X.J. (2008, January 16\u201318). The EEG Signal Preprocessing Based on Empirical Mode Decomposition. Proceedings of the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China.","DOI":"10.1109\/ICBBE.2008.862"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"8420","DOI":"10.1109\/JSEN.2018.2872623","article-title":"A Novel EEMD-CCA Approach to Removing Muscle Artifacts for Pervasive EEG","volume":"19","author":"Chen","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"13284","DOI":"10.1109\/ACCESS.2019.2892622","article-title":"A Minimum Arclength Method for Removing Spikes in Empirical Mode Decomposition","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1016\/j.bbe.2021.08.003","article-title":"An improved MAMA-EMD for the automatic removal of EOG artifacts","volume":"41","author":"Li","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"10584","DOI":"10.1109\/ACCESS.2019.2962658","article-title":"EEG signals denoising using optimal wavelet transform hybridized with efficient metaheuristic methods","volume":"8","author":"Alyasseri","year":"2019","journal-title":"IEEE Access"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Peng, J., Xu, J., and Bouridane, A. (2023). Survey of Explainable AI Techniques in Healthcare. Sensors, 23.","DOI":"10.3390\/s23020634"},{"key":"ref_88","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., and Wattenberg, M. (2017). Smoothgrad: Removing noise by adding noise. arXiv."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"102545","DOI":"10.1016\/j.artmed.2023.102545","article-title":"Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism","volume":"143","author":"Torres","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_90","unstructured":"Mansour, M., Khnaisser, F., and Partamian, H. (2020). An explainable model for eeg seizure detection based on connectivity features. arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.jneumeth.2016.10.008","article-title":"Interpretable deep neural networks for single-trial EEG classification","volume":"274","author":"Sturm","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref_92","unstructured":"Hartmann, K.G., Schirrmeister, R.T., and Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: A systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_94","unstructured":"Boashash, B., Carson, H., and Mesbah, M. (2000, January 16). Detection of seizures in newborns using time-frequency analysis of EEG signals. Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496), Pocono Manor, PA, USA."},{"key":"ref_95","unstructured":"Hassanpour, H., Mesbah, M., and Boashash, B. (2004, January 17\u201321). EEG spike detection using time-frequency signal analysis. Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s11045-022-00822-2","article-title":"Convolutional Neural Networks Based Time-Frequency Image Enhancement For the Analysis of EEG Signals","volume":"33","author":"Khan","year":"2022","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s11760-019-01615-0","article-title":"Seizure prediction with cross-higher-order spectral analysis of EEG signals","volume":"14","author":"Mahmoodian","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Murariu, M.G., T\u0103rniceriu, D., Hri\u0219c\u0103-Eva, O.D., and Laz\u0103r, A.M. (July, January 30). An Approach to Identify Different Types of EEG Epileptic Signals Based on Higher-Order Spectra (HOS) Features. Proceedings of the 2022 14th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Ploiesti, Romania.","DOI":"10.1109\/ECAI54874.2022.9847451"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s10916-010-9633-6","article-title":"Classification of epilepsy using high-order spectra features and principle component analysis","volume":"36","author":"Du","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_100","first-page":"25","article-title":"Analysis of the EEG Signal Using Higher-Order Spectra (HOS) in the Neuro-marketing Application","volume":"12","author":"Hosseini","year":"2022","journal-title":"New Mark. Res. J."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Li, S., and Cha, S.H. (2019, January 2\u20133). Feature extraction based on high order statistics measures and entropy for eeg biometrics. Proceedings of the 2019 7th International Workshop on Biometrics and Forensics (IWBF), Cancun, Mexico.","DOI":"10.1109\/IWBF.2019.8739183"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Xu, C., Wang, H., and Huang, J. (2022, January 18\u201320). The Analysis of EEG Signals in Driving Behavior Based on Nonlinear Dynamics. Proceedings of the 2022 International Symposium on Control Engineering and Robotics (ISCER), Changsha, China.","DOI":"10.1109\/ISCER55570.2022.00054"},{"key":"ref_103","first-page":"561","article-title":"Research on nonlinear dynamics of high-frequency EEG based on correlation dimension and Lyapunov exponent","volume":"17","author":"Shuchun","year":"2017","journal-title":"Mod. Biomed. Prog."},{"key":"ref_104","unstructured":"Dongmei, L. (2017). Research on Classification, Location and Prediction Methods of Epileptic EEG Signals Based on Nonlinear Dynamic Characteristics. [Master\u2019s Thesis, Xinjiang Medical University]."},{"key":"ref_105","first-page":"758","article-title":"Research on the complexity of Lempel Ziv EEG signals in emotion recognition","volume":"45","author":"Dongwei","year":"2014","journal-title":"J. Taiyuan Univ. Technol."},{"key":"ref_106","first-page":"2051","article-title":"Emotional EEG signal analysis based on equal symbolic entropy","volume":"35","author":"Meng","year":"2018","journal-title":"Comput. Appl. Res."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.ins.2022.07.121","article-title":"EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method","volume":"610","author":"Anuragi","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_108","first-page":"716","article-title":"Analysis of EEG signals using nonlinear dynamics: A review","volume":"41","author":"Sharma","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Hazarika, B.B., Gupta, D., and Kumar, B. (2023). EEG Signal Classification Using a Novel Universum-Based Twin Parametric-Margin Support Vector Machine. Cogn. Comput., 1\u201316.","DOI":"10.1007\/s12559-023-10115-w"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, R., Zheng, H., Zhu, H., and Shi, C.J.R. (2023). SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification. IEEE Trans. Neural Netw. Learn. Syst., early access.","DOI":"10.1109\/TNNLS.2023.3252569"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.inffus.2022.12.019","article-title":"Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques","volume":"92","author":"Hassan","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/0361-9230(95)02023-5","article-title":"Event related potentials during object recognition tasks","volume":"38","author":"Zhang","year":"1995","journal-title":"Brain Res. Bull."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"Deap: A database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3389\/fnins.2012.00039","article-title":"Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b","volume":"6","author":"Ang","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3389\/fnins.2012.00055","article-title":"Review of the BCI competition IV","volume":"6","author":"Tangermann","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TNSRE.2003.814453","article-title":"A data analysis competition to evaluate machine learning algorithms for use in brain\u2013computer interfaces","volume":"11","author":"Sajda","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state","volume":"64","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev. E"},{"key":"ref_119","unstructured":"Shoeb, A.H. (2009). Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Detti, P., Vatti, G., and Zabalo Manrique de Lara, G. (2020). Eeg synchronization analysis for seizure prediction: A study on data of noninvasive recordings. Processes, 8.","DOI":"10.3390\/pr8070846"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/THMS.2014.2366914","article-title":"Recognition of mental workload levels under complex human\u2013machine collaboration by using physiological features and adaptive support vector machines","volume":"45","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"101787","DOI":"10.1016\/j.artmed.2019.101787","article-title":"A Novel Method of motor imagery classification using eeg signal","volume":"103","author":"Venkatachalam","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhao, M., Wei, C., Mantini, D., Li, Z., and Liu, Q. (2020). EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising. arXiv.","DOI":"10.1088\/1741-2552\/ac2bf8"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","article-title":"A review on machine learning for EEG signal processing in bioengineering","volume":"14","author":"Hosseini","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s11760-020-01767-4","article-title":"A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier","volume":"15","author":"Choubey","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_126","first-page":"6","article-title":"Support vector classifier for EEG signals based on nonlinear feature extraction","volume":"24","author":"Ping","year":"2009","journal-title":"J. Shantou Univ. Nat. Sci. Ed."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Jamunadevi, C., Ragupathy, P., Sritha, P., Pandikumar, S., and Deepa, S. (2022, January 4\u20135). Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal. Proceedings of the 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India.","DOI":"10.1109\/ICACTA54488.2022.9753364"},{"key":"ref_128","first-page":"79","article-title":"Noise benefit in motion imagination classification based on K-nearest neighbor","volume":"32","author":"Jiahui","year":"2022","journal-title":"Comput. Technol. Dev."},{"key":"ref_129","unstructured":"Jiaying, L., Li, Z., Yan, B., and Fangqing, G. (2021). Research on classification of lower limb motor imagery EEG signals based on LDA and KNN. Foreign Electron. Meas. Technol., 1."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Dongare, S., and Padole, D. (2021, January 27\u201328). Categorization of EEG Using Hybrid Features and Voting classifier for Motor Imagination. Proceedings of the 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT52294.2021.9573666"},{"key":"ref_131","first-page":"4","article-title":"The application of decision tree algorithm in the prediction of stroke risk classification","volume":"28","author":"Ren","year":"2019","journal-title":"Chin. Conval. Med."},{"key":"ref_132","first-page":"1182","article-title":"Building a risk prediction model for ischemic stroke in Jiangxi based on machine learning","volume":"34","author":"Huaiwen","year":"2022","journal-title":"West. Med."},{"key":"ref_133","unstructured":"Hanqi, C., Hao, Z., Xiaomin, G., Mingyang, P., Guanghui, X., Guozhong, C., Xindao, Y., and Yu, X. (2022). Prediction of prognosis of mechanical thrombectomy in acute stroke by machine learning combined with imaging features. J. Nanjing Med. Univ. Nat. Sci. Ed., 42."},{"key":"ref_134","first-page":"5","article-title":"Explainable machine learning model is used to predict long-term cerebral ischemic events","volume":"22","author":"Yong","year":"2022","journal-title":"Prev. Treat. Cardiovasc. Cerebrovasc. Dis."},{"key":"ref_135","unstructured":"Carrara, I., and Papadopoulo, T. (2023). Classification of BCI-EEG based on augmented covariance matrix. arXiv."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Alharbi, Y.F., and Alotaibi, Y.A. (2021, January 22\u201324). The Correlate of Emotion and Gender Classification Using EEG Signals. Proceedings of the 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), Nanjing, China.","DOI":"10.1109\/ICSIP52628.2021.9688884"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Parmar, S.K., Ramwala, O.A., and Paunwala, C.N. (October, January 30). Performance Evaluation of SVM with Non-Linear Kernels for EEG-based Dyslexia Detection. Proceedings of the 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC), Bangalore, India.","DOI":"10.1109\/R10-HTC53172.2021.9641696"},{"key":"ref_138","unstructured":"Ling, H., and Aihua, Z. (2010). Application of improved decision tree SVM in EEG recognition. Comput. Eng. Des., 2."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Hossain, M.S., Hasan, T., Hasan, M.M., Rahman, M.M., and Sabiha, M.M. (2022, January 26\u201327). English Character recognition using EEG-based Visual stimulations: A Machine Learning Analysis. Proceedings of the 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh.","DOI":"10.1109\/ICISET54810.2022.9775921"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Padayatty, R.V., and K, T.F.N. (2022, January 21\u201322). Detection of schizophrenia using EEG signals: A Machine learning approach. Proceedings of the 2022 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), Malappuram, India.","DOI":"10.1109\/ICFCR54831.2022.9893701"},{"key":"ref_141","unstructured":"Yuehua, G., and Jinxiang, S. (2022). Classification of vertigo states combined with machine learning and EEG signal analysis. China Tissue Eng. Res., 26."},{"key":"ref_142","unstructured":"Shuyi, Z., Xiaoyan, L., Jiansong, Z., and Gang, S. (2018). Eeg signal analysis method based on standard time-frequency transform. J. Electron. Meas. Instrum., 7."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Satyanarayana, K.N.V., Shankar, T., Poojita, G., Vinay, G., Amaranadh, H.N.S.V.l.S., and Babu, A.G. (2022, January 29\u201331). An Approach to EEG based Emotion Identification by SVM classifier. Proceedings of the 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC53470.2022.9753699"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"6602","DOI":"10.1109\/TII.2022.3167470","article-title":"EEG-based driver fatigue detection using FAWT and multiboosting approaches","volume":"18","author":"Subasi","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_145","first-page":"8859","article-title":"Fusion of forehead EEG with machine vision for real-time fatigue detection in an automatic processing pipeline","volume":"35","author":"Min","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1038\/s41598-023-29647-0","article-title":"Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight","volume":"13","author":"Wilson","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MIM.2021.9448258","article-title":"A task agnostic mental fatigue assessment approach based on EEG frequency bands for demanding maritime operation","volume":"24","author":"Monteiro","year":"2021","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"104151","DOI":"10.1016\/j.autcon.2022.104151","article-title":"EEG-based work experience prediction using hazard recognition","volume":"136","author":"Wang","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"026005","DOI":"10.1088\/1741-2552\/abda0c","article-title":"A study on CNN image classification of EEG signals represented in 2D and 3D","volume":"18","author":"Bird","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Morabito, F.C., Campolo, M., Ieracitano, C., Ebadi, J.M., Bonanno, L., Bramanti, A., Desalvo, S., Mammone, N., and Bramanti, P. (2016, January 7\u20139). Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer\u2019s disease patients from scalp EEG recordings. Proceedings of the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, Italy.","DOI":"10.1109\/RTSI.2016.7740576"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"1650039","DOI":"10.1142\/S0129065716500398","article-title":"Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia","volume":"27","author":"Morabito","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"120054","DOI":"10.1016\/j.neuroimage.2023.120054","article-title":"Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset","volume":"272","author":"Kim","year":"2023","journal-title":"NeuroImage"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Kunekar, P.R., Gupta, M., and Agarwal, B. (2020, January 7\u20138). Deep Learning with Multi Modal Ensemble Fusion for Epilepsy Diagnosis. Proceedings of the 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India.","DOI":"10.1109\/ICETCE48199.2020.9091742"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Sagga, D., Echtioui, A., Khemakhem, R., Kallel, F., and Hamida, A.B. (2022, January 24\u201327). Epileptic Seizures Detection on EEG Signal Using Deep Learning Techniques. Proceedings of the 2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sfax, Tunisia.","DOI":"10.1109\/ATSIP55956.2022.9805860"},{"key":"ref_155","unstructured":"Qing, X., Cheng, G., Biao, C., and Shan, C. (2022). Classification of epileptic EEG signals based on deep learning. Data Acquis. Process., 037."},{"key":"ref_156","unstructured":"Ouyu, C., Yijun, L., Wujian, Y., Zhiwei, M., and Qi, L. (2019). Information and Computer (Theoretical Edition)."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Kumar, S., and Sengupta, A. (2022, January 24\u201325). EEG Classification For Stroke Detection Using Deep Learning Networks. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), Patna, India.","DOI":"10.1109\/ICEFEET51821.2022.9847883"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"73804","DOI":"10.1109\/ACCESS.2022.3190502","article-title":"Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning","volume":"10","author":"Rafiei","year":"2022","journal-title":"IEEE Access"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Sudhakar, T., Hari Krishnan, G., Krishnamoorthy, N.R., Janney J, B., Pradeepa, M., and Raghavi, J.P. (2021, January 25\u201327). Sleep Disorder Diagnosis using EEG based Deep Learning Techniques. Proceedings of the 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India.","DOI":"10.1109\/ICBSII51839.2021.9445158"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"26554","DOI":"10.1109\/ACCESS.2022.3154899","article-title":"Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG","volume":"10","author":"Leino","year":"2022","journal-title":"IEEE Access"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Kang, M.K., and Hong, K.S. (2022, January 4\u20137). A sleep stage classification method using deep learning by extracting the characteristics of frequency domain from a single EEG channel. Proceedings of the 2022 13th Asian Control Conference (ASCC), Jeju, Republic of Korea.","DOI":"10.23919\/ASCC56756.2022.9828168"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Almogbel, M.A., Dang, A.H., and Kameyama, W. (2018, January 11\u201314). EEG-signals based cognitive workload detection of vehicle driver using deep learning. Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon, Republic of Korea.","DOI":"10.23919\/ICACT.2018.8323715"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Bhardwaj, R., Parameswaran, S., and Balasubramanian, V. (2018, January 1\u20132). Performance Comparison of Machine Learning and Deep Learning While Classifying Driver\u2019s Cognitive State. Proceedings of the 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), Rupnagar, India.","DOI":"10.1109\/ICIINFS.2018.8721374"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Roy, A.D., and Islam, M.M. (2020, January 19\u201321). Detection of Epileptic Seizures from Wavelet Scalogram of EEG Signal Using Transfer Learning with AlexNet Convolutional Neural Network. Proceedings of the 2020 23rd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCIT51783.2020.9392720"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"103295","DOI":"10.1016\/j.bspc.2021.103295","article-title":"Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator","volume":"72","author":"Ak","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Bano, K.S., Bhuyan, P., and Ray, A. (2022, January 1\u20133). EEG-Based Brain Computer Interface for Emotion Recognition. Proceedings of the 2022 5th International Conference on Computational Intelligence and Networks (CINE), Bhubaneswar, India.","DOI":"10.1109\/CINE56307.2022.10037255"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Miao, Z., Zhang, X., Zhao, M., and Ming, D. (2023). LMDA-Net: A lightweight multi-dimensional attention network for general EEG-based brain\u2013computer interface paradigms and interpretability. arXiv.","DOI":"10.1016\/j.neuroimage.2023.120209"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Clerc, M., Bougrain, L., and Lotte, F. (2016). Brain-Computer Interfaces 1: Methods and Perspectives, John Wiley & Sons.","DOI":"10.1002\/9781119144977"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Corley, I.A., and Huang, Y. (2018, January 4\u20137). Deep EEG super-resolution: Upsampling EEG spatial resolution with generative adversarial networks. Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333379"},{"key":"ref_171","unstructured":"Wang, F., Zhong, S.H., Peng, J., Jiang, J., and Liu, Y. (2018, January 5\u20137). Data augmentation for EEG-based emotion recognition with deep convolutional neural networks. Proceedings of the MultiMedia Modeling: 24th International Conference, MMM 2018, Bangkok, Thailand."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2020.09.017","article-title":"A review on transfer learning in EEG signal analysis","volume":"421","author":"Wan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1109\/JAS.2022.105515","article-title":"Multi-modal domain adaptation variational autoencoder for eeg-based emotion recognition","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Voigt, P., and Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR), Springer. A Practical Guide.","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/JAS.2023.123123","article-title":"Explainable, domain-adaptive, and federated artificial intelligence in medicine","volume":"10","author":"Chaddad","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Sun, L., and Wu, J. (2022). A scalable and transferable federated learning system for classifying healthcare sensor data. IEEE J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2022.3171402"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Yildirim, O., Baloglu, U.B., and Acharya, U.R. (2019). A deep learning model for automated sleep stages classification using PSG signals. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16040599"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:48Z","timestamp":1760127168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,16]]},"references-count":178,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146434"],"URL":"https:\/\/doi.org\/10.3390\/s23146434","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,16]]}}}