{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:32:40Z","timestamp":1781886760090,"version":"3.54.5"},"reference-count":75,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T00:00:00Z","timestamp":1534723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Seed-Grant program at the German Jordanian University","award":["SAMS 8\/2014"],"award-info":[{"award-number":["SAMS 8\/2014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of     73.8 %    \u2013    86.2 %    . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.<\/jats:p>","DOI":"10.3390\/s18082739","type":"journal-article","created":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T11:23:06Z","timestamp":1534764186000},"page":"2739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-0231","authenticated-orcid":false,"given":"Rami","family":"Alazrai","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasha","family":"Homoud","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisham","family":"Alwanni","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, Freiburg 79098, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-5769","authenticated-orcid":false,"given":"Mohammad I.","family":"Daoud","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Doukas, C., and Maglogiannis, I. (2008). Intelligent pervasive healthcare systems. Advanced Computational Intelligence Paradigms in Healthcare-3, Springer.","DOI":"10.1007\/978-3-540-77662-8_5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TITB.2009.2034649","article-title":"Emotion Recognition From EEG Using Higher Order Crossings","volume":"14","author":"Petrantonakis","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Purnamasari, P.D., Ratna, A.A.P., and Kusumoputro, B. (2017). Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks. Algorithms, 10.","DOI":"10.3390\/a10020063"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s00779-017-1072-7","article-title":"Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset","volume":"21","author":"Menezes","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.asoc.2015.01.007","article-title":"Electroencephalogram-based emotion assessment system using ontology and data mining techniques","volume":"30","author":"Chen","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_6","unstructured":"Bourel, F., Chibelushi, C.C., and Low, A.A. (2002, January 20\u201321). Robust facial expression recognition using a state-based model of spatially-localised facial dynamics. Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/S1077-3142(03)00081-X","article-title":"Facial expression recognition from video sequences: Temporal and static modeling","volume":"91","author":"Cohen","year":"2003","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alazrai, R., and Lee, C.G. (2012, January 14\u201318). Real-time emotion identification for socially intelligent robots. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224587"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Alazrai, R., and Lee, C.G. (2012, January 7\u201312). An narx-based approach for human emotion identification. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385544"},{"key":"ref_10","unstructured":"Schuller, B., Reiter, S., Muller, R., Al-Hames, M., Lang, M., and Rigoll, G. (2005, January 6). Speaker independent speech emotion recognition by ensemble classification. Proceedings of the IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, F., Chang, E., Xu, Y.Q., and Shum, H.Y. (2001). Emotion detection from speech to enrich multimedia content. Pacific-Rim Conference on Multimedia, Springer.","DOI":"10.1007\/3-540-45453-5_71"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Poria, S., Chaturvedi, I., Cambria, E., and Hussain, A. (2016, January 12\u201315). Convolutional MKL based multimodal emotion recognition and sentiment analysis. Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0055"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/34.954607","article-title":"Toward machine emotional intelligence: Analysis of affective physiological state","volume":"23","author":"Picard","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","unstructured":"Nasoz, F., Alvarez, K., Lisetti, C.L., and Finkelstein, N. (2003, January 22\u201326). Emotion recognition from physiological signals for user modeling of affect. Proceedings of the UM 2003, 9th International Conference on User Model, Pittsburg, PA, USA."},{"key":"ref_15","unstructured":"Nie, D., Wang, X.W., Shi, L.C., and Lu, B.L. (May, January 27). EEG-based emotion recognition during watching movies. Proceedings of the 5th International IEEE\/EMBS Conference on Neural Engineering (NER), Cancun, Mexico."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1016\/j.neuroimage.2012.01.060","article-title":"The dynamics of EEG gamma responses to unpleasant visual stimuli: From local activity to functional connectivity","volume":"60","author":"Martini","year":"2012","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2016.12.005","article-title":"Recognition of Emotions Using Multimodal Physiological Signals and an Ensemble Deep Learning Model","volume":"140","author":"Yin","year":"2017","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Alwanni, H., Baslan, Y., Alnuman, N., and Daoud, M.I. (2017). EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution. Sensors, 17.","DOI":"10.3390\/s17091937"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Castiglioni, P. (2005). Choi-Williams Distribution. Encyclopedia of Biostatistics, John Wiley & Sons, Ltd.","DOI":"10.1002\/0470011815.b2a12012"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Boubchir, L., Al-Maadeed, S., and Bouridane, A. (2014, January 4\u20139). On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854733"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time-frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_24","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":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H., and Yan, B. (2017). Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain. BioMed Res. Int., 2017.","DOI":"10.1155\/2017\/8317357"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, W., Zheng, W.L., and Lu, B.L. (arXiv, 2016). Multimodal emotion recognition using multimodal deep learning, arXiv.","DOI":"10.1007\/978-3-319-46672-9_58"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rozgic, V., Vitaladevuni, S.N., and Prasad, R. (2013, January 26\u201331). Robust EEG emotion classification using segment level decision fusion. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6637858"},{"key":"ref_28","unstructured":"Chung, S.Y., and Yoon, H.J. (2012, January 17\u201321). Affective classification using Bayesian classifier and supervised learning. Proceedings of the 2012 12th International Conference on Control, Automation and Systems, JeJu Island, Korea."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., and Bhattacharya, S. (2017, January 6\u20139). Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset. Proceedings of the Twenty-Ninth AAAI Conference on Innovative Applications, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"627892","DOI":"10.1155\/2014\/627892","article-title":"EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation","volume":"2014","author":"Jirayucharoensak","year":"2014","journal-title":"Sci. World J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2015.10.049","article-title":"Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers","volume":"47","author":"Atkinson","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_32","unstructured":"Zheng, W.L., Zhu, J.Y., and Lu, B.L. (2017). Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Trans. Affect. Comput."},{"key":"ref_33","unstructured":"Kim, K.J., Kim, H., and Baek, N. (2018). EEG Based Classification of Human Emotions Using Discrete Wavelet Transform. IT Convergence and Security 2017, Springer."},{"key":"ref_34","unstructured":"Niedermeyer, E., and da Silva, F.L. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins."},{"key":"ref_35","unstructured":"Toole, J.M.O. (2009). Discrete Quadratic Time-Frequency Distributions: Definition, Computation, and a Newborn Electroencephalogram Application. [Ph.D. Thesis, School of Medicine, The University of Queensland]."},{"key":"ref_36","unstructured":"Boashash, B. (2015). Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Academic Press."},{"key":"ref_37","unstructured":"Alazrai, R., Aburub, S., Fallouh, F., and Daoud, M.I. (December, January 30). EEG-based BCI system for classifying motor imagery tasks of the same hand using empirical mode decomposition. Proceedings of the 10th IEEE International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1121\/1.1916342","article-title":"The Sound Spectrograph","volume":"18","author":"Koenig","year":"1946","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_39","unstructured":"Mallat, S. (2008). A Wavelet Tour of Signal Processing: The Sparse Way, Academic Press."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.knosys.2016.05.027","article-title":"Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study","volume":"106","author":"Boashash","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MSP.2013.2265914","article-title":"Time-Frequency Processing of Nonstationary Signals: Advanced TFD Design to Aid Diagnosis with Highlights from Medical Applications","volume":"30","author":"Boashash","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_42","unstructured":"Hahn, S.L. (1996). Hilbert Transforms in Signal Processing, Artech House."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/ASSP.1989.28057","article-title":"Improved time-frequency representation of multicomponent signals using exponential kernels","volume":"37","author":"Choi","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_44","unstructured":"Swami, A., Mendel, J., and Nikias, C. (2000). Higher-Order Spectra Analysis (HOSA) Toolbox, Version 2.0.3, Signals & Systems, Inc."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/1687-6180-2012-117","article-title":"A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals","volume":"2012","author":"Boashash","year":"2012","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2009.09.019","article-title":"Recognition of human activities using SVM multi-class classifier","volume":"31","author":"Qian","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kre\u00dfel, U.H.G. (1999). Pairwise classification and support vector machines. Advances in Kernel Methods, MIT Press.","DOI":"10.7551\/mitpress\/1130.003.0020"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A comparison of methods for multiclass support vector machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, D., Xie, S., Li, Y., Zhao, D., and El-Alfy, E.S.M. (2017). Brain Effective Connectivity Analysis from EEG for Positive and Negative Emotion. Neural Information Processing, Springer International Publishing.","DOI":"10.1007\/978-3-319-70139-4"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, X., Yan, J.Z., and Chen, J.H. (2017, January 24\u201326). Channel Division Based Multiple Classifiers Fusion for Emotion Recognition Using EEG Signals. Proceedings of the 2017 International Conference on Information Science and Technology, Wuhan, China.","DOI":"10.1051\/itmconf\/20171107006"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1109\/TITB.2011.2157933","article-title":"A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition","volume":"15","author":"Petrantonakis","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1111\/1469-8986.3860912","article-title":"Voluntary facial expression and hemispheric asymmetry over the frontal cortex","volume":"38","author":"Coan","year":"2001","journal-title":"Psychophysiology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.cmpb.2015.07.006","article-title":"Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals","volume":"122","author":"Khezri","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_56","unstructured":"Niemic, C.P., and Warren, K. (2002). Studies of Emotion, JUR. A Theoretical and Empirical Review of Psychophysiological Studies of Emotion."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TBME.2010.2048568","article-title":"EEG-Based Emotion Recognition in Music Listening","volume":"57","author":"Lin","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1007\/s00521-015-2149-8","article-title":"Wavelet-based emotion recognition system using EEG signal","volume":"28","author":"Mohammadi","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"Ding","year":"2005","journal-title":"J. Bioinform. Comput. Biol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Radovic, M., Ghalwash, M., Filipovic, N., and Obradovic, Z. (2017). Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform., 18.","DOI":"10.1186\/s12859-016-1423-9"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature Extraction and Selection for Emotion Recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhuang, N., Zeng, Y., Yang, K., Zhang, C., Tong, L., and Yan, B. (2018). Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals. Sensors, 18.","DOI":"10.3390\/s18030841"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, M., Zhao, S., Hu, S., Shi, Z., and Cao, Y. (2016). Relieff-based EEG sensor selection methods for emotion recognition. Sensors, 16.","DOI":"10.3390\/s16101558"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Momani, M., and Daoud, M.I. (2017). Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation. Appl. Sci., 7.","DOI":"10.3390\/app7040316"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Momani, M., Khudair, H.A., and Daoud, M.I. (2017). EEG-based tonic cold pain recognition system using wavelet transform. Neural Comput. Appl.","DOI":"10.1007\/s00521-017-3263-6"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1016\/j.compbiomed.2013.10.017","article-title":"EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm","volume":"43","author":"Yoon","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/S0013-4694(97)00147-8","article-title":"Volume conduction effects in EEG and MEG","volume":"106","author":"Reinders","year":"1998","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Liao, K., Xiao, R., Gonzalez, J., and Ding, L. (2014). Decoding individual finger movements from one hand using human EEG signals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085192"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1007\/s11042-015-3119-y","article-title":"Affect representation and recognition in 3D continuous valence-arousal-dominance space","volume":"76","author":"Verma","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1016\/j.ins.2007.11.012","article-title":"Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface","volume":"178","author":"Zhou","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.patcog.2014.08.016","article-title":"Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection","volume":"48","author":"Boashash","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/S0165-1684(00)00236-X","article-title":"A measure of some time-frequency distributions concentration","volume":"81","year":"2001","journal-title":"Signal Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2015.08.004","article-title":"Application of entropies for automated diagnosis of epilepsy using EEG signals: A review","volume":"88","author":"Acharya","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.knosys.2016.01.040","article-title":"Automated detection and localization of myocardial infarction using electrocardiogram: A comparative study of different leads","volume":"99","author":"Acharya","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","article-title":"Deep learning for healthcare applications based on physiological signals: A review","volume":"161","author":"Faust","year":"2018","journal-title":"Comput. 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