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Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch\u2019s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.<\/jats:p>","DOI":"10.3390\/s20082403","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T10:46:22Z","timestamp":1587638782000},"page":"2403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analyzing the Effectiveness of the Brain\u2013Computer Interface for Task Discerning Based on Machine Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Jakub","family":"Browarczyk","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-3016","authenticated-orcid":false,"given":"Adam","family":"Kurowski","sequence":"additional","affiliation":[{"name":"Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"},{"name":"Audio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6288-2908","authenticated-orcid":false,"given":"Bozena","family":"Kostek","sequence":"additional","affiliation":[{"name":"Audio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11\/12, 80-233 Gdansk, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Caba\u00f1ero-G\u00f3mez, L., Hervas, R., Bravo, J., and Rodriguez-Benitez, L. (2018). Computational EEG Analysis Techniques When Playing Video Games: A Systematic Review. Proceedings, 2.","DOI":"10.3390\/proceedings2190483"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1515\/REVNEURO.2010.21.6.451","article-title":"EEG-based Brain\u2013computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation","volume":"21","author":"Machado","year":"2010","journal-title":"Rev. Neurosci."},{"key":"ref_4","first-page":"201","article-title":"Electroencephalographic patterns in coma: When things slow down","volume":"29","author":"Kaplan","year":"2012","journal-title":"Epileptologie"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1093\/brain\/awt077","article-title":"Brain\u2013computer interfacing: Science fiction has come true","volume":"136","year":"2013","journal-title":"Brain"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Choubey, H., and Pandey, A. (2018). A new feature extraction and classification mechanisms for EEG signal processing. Multidim. Syst. Sign. Process., 30.","DOI":"10.1007\/s11045-018-0628-7"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1109\/TNNLS.2018.2886414","article-title":"EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation","volume":"30","author":"Gao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cmpb.2005.06.011","article-title":"Dynamical analysis of EEG signals at various sleep stages","volume":"80","author":"Acharya","year":"2005","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kannathal, N., Acharya, U.R., Fadilah, A., Tibelong, T., and Sadasivan, P.K. (2004). Nonlinear analysis of EEG signals at different mental states. Biomed. Eng. Online, 3.","DOI":"10.1186\/1475-925X-3-7"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain computer interfaces, a review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, B. (2012). Brain\u2013computer interfaces. Neural Engineering, Springer.","DOI":"10.1007\/978-1-4614-5227-0_2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1109\/TBME.2014.2312397","article-title":"Brain\u2013computer interfaces using sensorimotor rhythms: Current state and future perspectives","volume":"61","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, J., Zhao, Y., Sun, H., Chen, J., Ke, A., Xu, G., Zhang, H., Zhou, J., and Wang, C. (2018). A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00217"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TNSRE.2006.875527","article-title":"Geometric subspace methods and time-delay embedding for EEG artifact removal and classification","volume":"14","author":"Charles","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mannan, M.M.N., Kamran, M.A., Kang, S., and Jeong, M.Y. (2018). Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain\u2013computer Interface: A Comprehensive Study. Complexity, 18\u201336.","DOI":"10.1155\/2018\/4853741"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","article-title":"EEG signal classification using wavelet feature extraction and a mixture of expert model","volume":"32","author":"Subasi","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_17","unstructured":"Calderon, H., and Sahonero-Alvarez, G. (2017, January 21\u201324). A Comparison of SOBI, FastICA, JADE and Infomax Algorithms. Proceedings of the 8th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2017), Orlando, FL, USA."},{"key":"ref_18","unstructured":"Himberg, J., and Hyv\u00e4rinen, A. (2003, January 17\u201319). Icasso: Software for investigating the reliability of ICA estimates by clustering and visualization. Proceedings of the IEEE 13th Workshop on Neural Networks for Signal Processing (NNSP\u201903), Toulouse, France."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hyv\u00e4rinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis, Wiley.","DOI":"10.1002\/0471221317"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1023\/A:1016568309421","article-title":"Recent approaches to global optimization problems through particle swarm optimization","volume":"116","author":"Parsopoulos","year":"2002","journal-title":"Nat. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/10.841330","article-title":"Independent component approach to the analysis of EEG and MEG recordings","volume":"47","author":"Oja","year":"2000","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1109\/TBME.2003.816076","article-title":"Temporally constrained ICA: An application to artifact rejection in electromagnetic brain signal analysis","volume":"50","author":"James","year":"2003","journal-title":"IEEE Trans Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"31","DOI":"10.20982\/tqmp.06.1.p031","article-title":"An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms","volume":"6","author":"Langlois","year":"2010","journal-title":"Tutor. Quant. Methods Psychol."},{"key":"ref_24","unstructured":"Palmer, J.A., Kreutz-Delgado, K., and Makeig, S. (2011). AMICA: An Adaptive Mixture of Independent Component Analyzers with Shared Components, University of California."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1097\/00004691-200307000-00004","article-title":"Independent component analysis as a tool to eliminate artifacts in EEG: A quantitative study","volume":"20","author":"Iriarte","year":"2003","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.4015\/S1016237205000263","article-title":"Meditation EEG interpretation based on novel fuzzy-merging strategies and wavelet features","volume":"17","author":"Chang","year":"2005","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jahankhani, P., Kodogiannis, V., and Revett, K. (2006, January 3\u20136). EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA\u201906), Sofia, Bulgaria.","DOI":"10.1109\/JVA.2006.17"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TPAMI.2012.69","article-title":"A Novel Bayesian Framework for Discriminative Feature Extraction in Brain\u2013computer Interfaces","volume":"35","author":"Suk","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miranda, E.R., and Castet, J. (2014). A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain\u2013computer Interfaces. Guide to Brain\u2013computer Music Interfacing, Springer.","DOI":"10.1007\/978-1-4471-6584-2"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"25399","DOI":"10.1109\/ACCESS.2018.2833746","article-title":"Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals","volume":"6","author":"Wen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yao, L., and Yuan, F. (2019, January 4\u20138). Adversarial Variational Embedding for Robust Semi-supervised Learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AL, USA.","DOI":"10.1145\/3292500.3330966"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1142\/S0129065719500023","article-title":"Regularized Group Sparse Discriminant Analysis for P300-Based Brain\u2013Computer Interface","volume":"29","author":"Wu","year":"2019","journal-title":"Int. J. Neural Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1016\/j.procs.2018.05.116","article-title":"Classification of EEG data for human mental state analysis using Random Forest Classifier","volume":"132","author":"Edla","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_35","unstructured":"Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., and Zhang, Y. (2019). A Survey on Deep Learning based Brain\u2013computer Interface: Recent Advances and New Frontiers. arXiv."},{"key":"ref_36","first-page":"1","article-title":"Comparison of the effectiveness of automatic EEG signal class separation algorithms","volume":"10","author":"Kurowski","year":"2019","journal-title":"J. Intel. Fuzzy Sys."},{"key":"ref_37","unstructured":"Bashivan, P., Rish, I., Yeasin, M., and Codella, N. (2015). Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. arXiv."},{"key":"ref_38","unstructured":"Gonfalonieri, A. (2020, April 10). Deep Learning Algorithms and Brain\u2013Computer Interfaces. Available online: https:\/\/towardsdatascience.com\/deep-learning-algorithms-and-brain\u2013computer-interfaces-7608d0a6f01."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3233\/FI-2019-1831","article-title":"Method for Clustering of Brain Activity Data Derived from EEG Signals","volume":"168","author":"Kurowski","year":"2019","journal-title":"Fundam. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10462-007-9052-3","article-title":"Machine learning: A review of classification and combining techniques","volume":"26","author":"Kotsiantis","year":"2006","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1023\/A:1009752403260","article-title":"On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach","volume":"1","author":"Salzberg","year":"1997","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_43","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":"Lu","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T.-P., and Lin, C.-T. (2020). Fellow, IEEE, EEG-based Brain\u2013computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications. arXiv.","DOI":"10.1109\/TCBB.2021.3052811"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.1109\/TNNLS.2015.2476656","article-title":"Sparse Bayesian Classification of EEG for Brain\u2013Computer Interface","volume":"27","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jebelli, H., Khalili, M.M., and Lee, S. (2019). Mobile EEG-based workers stress recognition by applying deep neural network. Advances in Informatics and Computing in Civil and Construction Engineering, Springer.","DOI":"10.1007\/978-3-030-00220-6_21"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Moon, S.-E., Jang, S., and Lee, J.-S. (2018, January 15\u201320). Convolutional neural network approach for EEG-based emotion recognition using brain connectivity and its spatial information. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461315"},{"key":"ref_49","unstructured":"(2020, April 11). DEAP Dataset. Available online: https:\/\/www.eecs.qmul.ac.uk\/mmv\/datasets\/deap\/."},{"key":"ref_50","unstructured":"Song, T., Zheng, W., Song, P., and Cui, Z. (2019). EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput., 1."},{"key":"ref_51","unstructured":"SEED Dataset (2020, April 11). BCMI Resources. Available online: http:\/\/bcmi.sjtu.edu.cn\/resource.html."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Attia, M., Hettiarachchi, I., Hossny, M., and Nahavandi, S. (2018, January 4\u20137). A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363685"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices","volume":"22","author":"Katsigiannis","year":"2018","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108312","DOI":"10.1016\/j.jneumeth.2019.108312","article-title":"Deep convolutional neural network for classification of sleep stages from single-channel EEG signals","volume":"324","author":"Mousavi","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_55","unstructured":"Moinnereau, M.-A., Brienne, T., Brodeur, S., Rouat, J., Whittingstall, K., and Plourde, E. (2018). Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Souly, N., and Shah, M. (2017, January 21\u201326). Deep learning human mind for automated visual classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.479"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.eswa.2018.08.031","article-title":"An end-to-end deep learning approach to MI-EEG signal classification for BCIs","volume":"114","author":"Dose","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Talathi, S.S. (2017). Deep recurrent neural networks for seizure detection and early seizure detection systems. arXiv.","DOI":"10.2172\/1366924"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","article-title":"Entropies for detection of epilepsy in EEG","volume":"80","author":"Kannathal","year":"2005","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Golmohammadi, M., Ziyabari, S., Shah, V., Lopez de Diego, S., Obeid, I., and Picone, J. (2017). Deep Architectures for Automated Seizure Detection in Scalp EEGs. arXiv.","DOI":"10.1109\/ICMLA.2018.00118"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Harati, A., Lopez, S., Obeid, I., Jacobson, M., Tobochnik, S., and Picone, J. (2014, January 13). THE TUH EEG CORPUS: A Big Data Resource for Automated EEG Interpretation. Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, Philadelphia, PE, USA.","DOI":"10.1109\/SPMB.2014.7002953"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ruffini, G., Ibanez, D., Castellano, M., Dunne, S., and Soria-Frisch, A. (2016, January 6\u20139). EEG-driven RNN classification for prognosis of neurodegeneration in at-risk patients. Proceedings of the International Conference on Artificial Neural Networks, Barcelona, Spain.","DOI":"10.1007\/978-3-319-44778-0_36"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","article-title":"Automated eeg-based screening of depression using deep convolutional neural network","volume":"161","author":"Acharya","year":"2018","journal-title":"Methods Programs Biomed."},{"key":"ref_65","unstructured":"Sheikhani, A., Behnam, H., Mohammadi, M.R., and Noorozian, M. (2007, January 22\u201323). Analysis of EEG background activity in Autism disease patients with bispectrum and STFT measure. Proceedings of the 11th Conference on 11t WSEAS International Conference on Communications, Madrid, Spain."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Jin, Z., Zhou, G., Gao, D., and Zhang, Y.L. (2018). EEG classification using sparse Bayesian extreme learning machine for brain\u2013computer interface. Neural Comput. Appl., 1\u20139.","DOI":"10.1007\/s00521-018-3735-3"},{"key":"ref_67","unstructured":"(2020, April 11). ADNI Data and Samples. Available online: http:\/\/adni.loni.usc.edu\/data-samples\/access-data\/."},{"key":"ref_68","unstructured":"(2020, April 11). AMIGOS Dataset. Available online: http:\/\/www.eecs.qmul.ac.uk\/mmv\/datasets\/amigos\/readme.html."},{"key":"ref_69","unstructured":"(2020, April 11). BCI Competitions. Available online: http:\/\/www.bbci.de\/competition\/."},{"key":"ref_70","unstructured":"(2020, April 11). BCI2000 Wiki. Available online: https:\/\/www.bci2000.org\/mediawiki\/index.php\/Main_Page."},{"key":"ref_71","unstructured":"(2020, April 11). CHB-MIT Scalp EEG Database. Available online: http:\/\/archive.physionet.org\/pn6\/chbmit\/."},{"key":"ref_72","unstructured":"(2020, April 11). EEG Resources. Available online: https:\/\/www.isip.piconepress.com\/projects\/tuh_eeg\/."},{"key":"ref_73","unstructured":"(2020, April 11). MICCAI BraTS 2018 Data. Available online: http:\/\/www.med.upenn.edu\/sbia\/brats2018\/data.html."},{"key":"ref_74","unstructured":"(2020, April 11). Montreal Archive of Sleep Studies. Available online: http:\/\/massdb.herokuapp.com\/en\/."},{"key":"ref_75","unstructured":"(2020, April 11). OpenMIIR Dataset. Available online: https:\/\/owenlab.uwo.ca\/research\/the_openmiir_dataset.html."},{"key":"ref_76","unstructured":"(2020, April 11). SHHS Polysomnography Database. Available online: http:\/\/archive.physionet.org\/pn3\/shhpsgdb\/."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Bembenik, R., Skonieczny, \u0141., Protaziuk, G., Krzyszkiewicz, M., and Rybinski, H. (2018). Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals. Intelligent Methods and Big Data in Industrial Applications, Springer.","DOI":"10.1007\/978-3-319-77604-0"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Stanczyk, U., Zielosko, B., and Jain, L.C. (2018). Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion. Advances in Feature Selection for Data and Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-67588-6"},{"key":"ref_79","unstructured":"(2020, March 12). Emotiv EPOC\u00b1Technical Specifications. Available online: https:\/\/emotiv.gitbook.io\/epoc-user-manual\/introduction-1\/technical_specifications."},{"key":"ref_80","first-page":"371","article-title":"The Ten-Twenty Electrode System of the International Federation","volume":"10","author":"Jasper","year":"1958","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0013-4694(97)00106-5","article-title":"IFCN standards for digital recording of clinical EEG","volume":"106","author":"Nuwer","year":"1999","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Gwizdka, J., Hosseini, R., Cole, M., and Wang, S. (2017). Temporal dynamics of eye-tracking and EEG during reading and relevance decisions. J. Assoc. Inf. Sci. Techol., 68.","DOI":"10.1002\/asi.23904"},{"key":"ref_83","first-page":"220","article-title":"Complex Encephalogram Dynamics during Meditation","volume":"2","author":"Joseph","year":"2007","journal-title":"J. Chin. Clin. Med."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0925-2312(01)00653-1","article-title":"Multiple comparison procedures applied to model selection","volume":"48","author":"Pizarro","year":"2002","journal-title":"Neurocomputing"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCSE.2007.58","article-title":"Python for scientific computing","volume":"9","author":"Oliphant","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_86","unstructured":"(2020, March 12). Keras Documentation. Available online: https:\/\/keras.io\/."},{"key":"ref_87","unstructured":"(2020, March 12). scikit-Learn Documentation. Available online: https:\/\/scikit-learn.org\/stable\/documentation.html."},{"key":"ref_88","unstructured":"(2020, March 12). TensorFlow Guide. Available online: https:\/\/www.tensorflow.org\/guide."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U. (1999, January 10\u201312). When Is Nearest Neighbor Meaningful?. Proceedings of the 7th International Conference on Database Theory (ICDT), Jerusalem, Israel.","DOI":"10.1007\/3-540-49257-7_15"},{"key":"ref_90","unstructured":"Pestov, V. (2012). Is the k-NN classifier in high dimensions affected by the curse of dimensionality?. arXiv."},{"key":"ref_91","unstructured":"Casella, G., Fienberg, S.E., and Olkin, I. (2013). Linear Mixed-Effects Models Using, R. A Step-by-Step Approach. Springer Texts in Statistics, Springer."},{"key":"ref_92","unstructured":"(2020, March 12). Online Documentation for the Statsmodels Method Used for Calculation of MLM-Based Statistical Tests. Available online: https:\/\/www.statsmodels.org\/devel\/mixed_glm.html."},{"key":"ref_93","unstructured":"Signorell, A., Aho, K., Alfons, A., Anderegg, N., Aragon, T., and Arppe, A. (2020, April 09). DescTools: Tools for Descriptive Statistics. R Package Version 0.99.34. Available online: https:\/\/cran.r-project.org\/package=DescTools."},{"key":"ref_94","unstructured":"Bengio, Y., Glorot, X., and Bordes, A. (2011, January 11\u201313). Deep Sparse Rectifier Neural Networks. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA."},{"key":"ref_95","unstructured":"Hinton, G.E., and Nair, V. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_97","unstructured":"Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 17\u201319). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning (ICML), Atlanta, GA, USA."},{"key":"ref_98","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_99","first-page":"2677","article-title":"An Extension on \u201cStatistical Comparisons of Classifiers over Multiple Data Sets\u201d for all Pairwise Comparisons","volume":"9","author":"Garcia","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/86.895948","article-title":"Using time-dependent neural networks for EEG classification","volume":"8","author":"Haselsteiner","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_101","unstructured":"Ziyabari, S., Shah, V., Golmohammadi, M., Obeid, I., and Picone, J. (2017). Objective evaluation metrics for automatic classification of EEG events. arXiv."},{"key":"ref_102","unstructured":"Lu, H., Wang, M., and Yu, H. (2005, January 1\u20134). EEG Model and Location in Brain when Enjoying Music. Proceedings of the 27th Annual IEEE Engineering in Medicine and Biology Conference, Shanghai, China."},{"key":"ref_103","unstructured":"Han, J., Kamber, M., and Jian, P. (2012). Data Mining: Concepts and Techniques, Morgan Kaufmann, Elsevier."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:08Z","timestamp":1760364548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,23]]},"references-count":103,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20082403"],"URL":"https:\/\/doi.org\/10.3390\/s20082403","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,23]]}}}