{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T01:13:38Z","timestamp":1770340418577,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universiti Kebangsaan Malaysia and Ministry of Education, Malaysia","award":["FRGS\/1\/2018\/TK04\/UKM\/02\/2"],"award-info":[{"award-number":["FRGS\/1\/2018\/TK04\/UKM\/02\/2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial      (  S S  )     , entropy-spatial      (  E S  )      and temporo-spatial      (  T S  )      emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson\u2019s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying     S S    ,     E S     and     T S     profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.<\/jats:p>","DOI":"10.3390\/s20010059","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers"],"prefix":"10.3390","volume":"20","author":[{"given":"Noor Kamal","family":"Al-Qazzaz","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"},{"name":"Department of Electrical, Electronic &amp; Systems Engineering, Faculty of Engineering &amp; Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7733-6168","authenticated-orcid":false,"given":"Mohannad K.","family":"Sabir","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-863X","authenticated-orcid":false,"given":"Sawal Hamid Bin Mohd","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic &amp; Systems Engineering, Faculty of Engineering &amp; Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1759-0118","authenticated-orcid":false,"given":"Siti Anom","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia"},{"name":"Malaysian Research Institute of Ageing (MyAgeing), Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia"}]},{"given":"Karl","family":"Grammer","sequence":"additional","affiliation":[{"name":"Department of Evolutionary Anthropology, University of Vienna, Althan strasse 14, A-1090 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/exsy.12014","article-title":"Electrocardiogram-based emotion recognition system using empirical mode decomposition and discrete Fourier transform","volume":"31","author":"Jerritta","year":"2014","journal-title":"Expert Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.procs.2018.04.056","article-title":"Learning emotions EEG-based recognition and brain activity: A survey study on BCI for intelligent tutoring system","volume":"130","author":"Xu","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"390","DOI":"10.4236\/jbise.2010.34054","article-title":"Classification of human emotion from EEG using discrete wavelet transform","volume":"3","author":"Murugappan","year":"2010","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_4","first-page":"1","article-title":"EEG-based emotion recognition","volume":"56","author":"Bos","year":"2006","journal-title":"Influ. Vis. Audit. Stimuli"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.neuroimage.2012.05.012","article-title":"Overlapping activity in anterior insula during interoception and emotional experience","volume":"62","author":"Zaki","year":"2012","journal-title":"Neuroimage"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1080\/026999398379574","article-title":"Discrete emotions or dimensions? The role of valence focus and arousal focus","volume":"12","author":"Barrett","year":"1998","journal-title":"Cogn. Emot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/02699939208411068","article-title":"An argument for basic emotions","volume":"6","author":"Ekman","year":"1992","journal-title":"Cogn. Emot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/BF02686918","article-title":"Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament","volume":"14","author":"Mehrabian","year":"1996","journal-title":"Curr. Psychol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2013.06.046","article-title":"Emotional state classification from EEG data using machine learning approach","volume":"129","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/02699930802204677","article-title":"Measures of emotion: A review","volume":"23","author":"Mauss","year":"2009","journal-title":"Cogn. Emot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.15171\/icnj.2018.26","article-title":"Emotion classification through nonlinear EEG analysis using machine learning methods","volume":"5","author":"Soroush","year":"2018","journal-title":"Int. Clin. Neurosci. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1515\/REVNEURO.2004.15.4.241","article-title":"Remembering emotional experiences: The contribution of valence and arousal","volume":"15","author":"Kensinger","year":"2004","journal-title":"Rev. Neurosci."},{"key":"ref_13","unstructured":"Vedran, K., Alex, L., and Munir, M. (2005). Emotion recognition through physiological signals for human-machine communication. Cutting Edge Robotics 2010, Intech Open. Available online: https:\/\/www.intechopen.com\/books\/cutting-edge-robotics-2010\/emotion-recognition-through-physiological-signals-for-human-machine-communication."},{"key":"ref_14","first-page":"2061","article-title":"A film set for the elicitation of emotion in research: A comprehensive catalog derived from four decades of investigation","volume":"6","author":"Shaheen","year":"2017","journal-title":"Behav. Res. methods"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/1475-925X-12-44","article-title":"Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst","volume":"12","author":"Selvaraj","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1080\/02699930903274322","article-title":"Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers","volume":"24","author":"Schaefer","year":"2010","journal-title":"Cogn. Emot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.24297\/ijct.v11i1.1190","article-title":"A study of physiological signals-based emotion recognition systems","volume":"11","author":"Ping","year":"2013","journal-title":"Int. J. Comput. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1088\/0967-3334\/32\/10\/002","article-title":"Comparison of blood volume pulse and skin conductance responses to mental and affective stimuli at different anatomical sites","volume":"32","author":"Kushki","year":"2011","journal-title":"Physiol. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.physbeh.2010.11.029","article-title":"The use of nasal skin temperature measurements in studying emotion in macaque monkeys","volume":"102","author":"Kuraoka","year":"2011","journal-title":"Physiol. Behav."},{"key":"ref_20","first-page":"76","article-title":"Cortical auditory evoked potentials as indicators of hearing aids performance in speech perception","volume":"5","author":"Santhosh","year":"2017","journal-title":"J. Eng. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/T-AFFC.2011.28","article-title":"ECG pattern analysis for emotion detection","volume":"3","author":"Agrafioti","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"K\u00fcnecke, J., Hildebrandt, A., Recio, G., Sommer, W., and Wilhelm, O. (2014). Facial EMG responses to emotional expressions are related to emotion perception ability. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0084053"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/s11517-011-0747-x","article-title":"Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements","volume":"49","author":"Vecchiato","year":"2011","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.biopsycho.2004.03.002","article-title":"Frontal EEG asymmetry as a moderator and mediator of emotion","volume":"67","author":"Coan","year":"2004","journal-title":"Biol. Psychol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Di Flumeri, G., Aric\u00f2, P., Borghini, G., Sciaraffa, N., Maglione, A.G., Rossi, D., Modica, E., Trettel, A., Babiloni, F., and Colosimo, A. (2017, January 11\u201315). EEG-based approach-withdrawal index for the pleasantness evaluation during taste experience in realistic settings. Proceedings of the 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037544"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions recognition using EEG signals: A survey","volume":"10","author":"Alarcao","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_28","unstructured":"Al-Qazzaz, N.K., Sabir, M.K., and Grammer, K. (2019, January 8\u201310). Gender differences identification from brain regions using spectral relative powers of emotional EEG. Proceedings of the 2019 7th International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Sabir, M.K., and Grammer, K. (2019, January 28\u201330). Correlation indices of electroencephalogram-based relative powers during human emotion processing. Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology, Tokyo, Japan.","DOI":"10.1145\/3326172.3326179"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Sabir, M.K., Ali, S., Ahmad, S.A., and Grammer, K. (2019, January 23\u201327). Effective EEG Channels for emotion identification over the brain regions using differential evolution algorithm. Proceedings of the 2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856854"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11517-012-0967-8","article-title":"Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis","volume":"51","author":"Xie","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1088\/0967-3334\/27\/3\/003","article-title":"Entropy analysis of the EEG background activity in Alzheimer\u2019s disease patients","volume":"27","author":"Hornero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N., Hamid Bin Mohd Ali, S., Ahmad, S., Islam, M., and Escudero, J. (2017). Automatic artifact removal in EEG of normal and demented individuals using ICA\u2013WT during working memory tasks. Sensors, 17.","DOI":"10.3390\/s17061326"},{"key":"ref_34","first-page":"1","article-title":"Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis","volume":"56","author":"Ali","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TBME.2007.893452","article-title":"EEG-based lapse detection with high temporal resolution","volume":"54","author":"Davidson","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/B978-0-7020-5307-8.00015-6","article-title":"Resting state cortical EEG rhythms in Alzheimer\u2019s disease: Toward EEG markers for clinical applications: A review","volume":"62","author":"Vecchio","year":"2012","journal-title":"Suppl. Clin. Neurophysiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Ali, S.H.B., Ahmad, S.A., Chellappan, K., Islam, M.S., and Escudero, J. (2014). Role of EEG as biomarker in the early detection and classification of dementia. Sci. World J., 2014.","DOI":"10.1155\/2014\/906038"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2560\/12\/3\/031001","article-title":"EEG artifact removal-state-of-the-art and guidelines","volume":"12","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13046","DOI":"10.3390\/s140713046","article-title":"Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique","volume":"14","author":"Reaz","year":"2014","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1017\/CBO9780511546396.003","article-title":"Electroencephalography and high-density electrophysiological source localization","volume":"3","author":"Pizzagalli","year":"2007","journal-title":"Handb. Psychophysiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"EEG dynamics in patients with Alzheimer\u2019s disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1126\/science.3336779","article-title":"Neurometrics: Computer-assisted differential diagnosis of brain dysfunctions","volume":"239","author":"John","year":"1988","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/0013-4694(93)90152-L","article-title":"Regional differences in brain electrical activity in dementia: Use of spectral power and spectral ratio measures","volume":"87","author":"Leuchter","year":"1993","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lizio, R., Vecchio, F., Frisoni, G.B., Ferri, R., Rodriguez, G., and Babiloni, C. (2011). Electroencephalographic rhythms in Alzheimer\u2019s disease. Int. J. Alzheimer\u2019s Dis., 2011.","DOI":"10.4061\/2011\/927573"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/1744-9081-10-12","article-title":"On the analysis of EEG power, frequency and asymmetry in Parkinson\u2019s disease during emotion processing","volume":"10","author":"Yuvaraj","year":"2014","journal-title":"Behav. Brain Funct."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., and Chen, X. (2018, January 8\u201313). Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/THC-174836","article-title":"Emotion recognition from multichannel EEG signals using K-nearest neighbor classification","volume":"26","author":"Li","year":"2018","journal-title":"Technol. Health Care"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chao, H., Zhi, H., Dong, L., and Liu, Y. (2018). Recognition of emotions using multichannel EEG data and DBN-GC-based ensemble deep learning framework. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/9750904"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1587\/transinf.2015EDP7251","article-title":"Continuous music-emotion recognition based on electroencephalogram","volume":"99","author":"Thammasan","year":"2016","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s12193-011-0080-6","article-title":"Real-time EEG-based emotion recognition for music therapy","volume":"5","author":"Sourina","year":"2012","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chandran, V., Acharya, R., and Lim, C. (2007, January 22\u201326). Higher order spectral (HOS) analysis of epileptic EEG signals. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353847"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00371-015-1183-y","article-title":"Real-time EEG-based emotion monitoring using stable features","volume":"32","author":"Lan","year":"2016","journal-title":"Vis. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"314","DOI":"10.3389\/fpsyt.2017.00314","article-title":"An integrated model of emotional problems, beta power of electroencephalography, and low frequency of heart rate variability after childhood trauma in a non-clinical sample: A path analysis study","volume":"8","author":"Jin","year":"2018","journal-title":"Front. Psychiatry"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00ednez, B., Mart\u00ednez-Rodrigo, A., Zangr\u00f3niz Cantabrana, R., Pastor Garc\u00eda, J., and Alcaraz, R. (2016). Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings. Entropy, 18.","DOI":"10.3390\/e18060221"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"24","DOI":"10.14257\/astl.2015.91.05","article-title":"Towards emotion recognition of EEG brain signals using Hjorth parameters and SVM","volume":"91","author":"Mehmood","year":"2015","journal-title":"Adv. Sci. Technol. Lett. Biosci. Med. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biopsycho.2018.06.008","article-title":"Fractal dimension of EEG signals and heart dynamics in discrete emotional states","volume":"137","year":"2018","journal-title":"Biol. Psychol."},{"key":"ref_57","unstructured":"Yuen, C.T., San San, W., Seong, T.C., and Rizon, M. (2009). Classification of human emotions from EEG signals using statistical features and neural network. Int. J. Integr. Eng., 1."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5083","DOI":"10.19026\/rjaset.5.4401","article-title":"Effectiveness of statistical features for human emotions classification using EEG biosensors","volume":"5","author":"Yuen","year":"2013","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.cmpb.2016.02.008","article-title":"Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation","volume":"128","author":"Azami","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3389\/fninf.2019.00040","article-title":"Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition","volume":"13","author":"Zunino","year":"2019","journal-title":"Front. Neuroinform."},{"key":"ref_62","first-page":"1021","article-title":"Comparative study of approximate entropy and sample entropy based on characterization of EEG","volume":"35","author":"Li","year":"2014","journal-title":"Comput. Eng. Des."},{"key":"ref_63","first-page":"92","article-title":"Motor imagery EEG feature extraction based on fuzzy entropy","volume":"41","author":"Tian","year":"2013","journal-title":"J. Huazhong Univ. Sci. Technol"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"083116","DOI":"10.1063\/1.4929148","article-title":"Characterization of complexity in the electroencephalograph activity of Alzheimer\u2019s disease based on fuzzy entropy","volume":"25","author":"Cao","year":"2015","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1007\/s11517-017-1647-5","article-title":"Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis","volume":"55","author":"Azami","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zheng, J., Tu, D., Pan, H., Hu, X., Liu, T., and Liu, Q. (2017). A refined composite multivariate multiscale fuzzy entropy and laplacian score-based fault diagnosis method for rolling bearings. Entropy, 19.","DOI":"10.3390\/e19110585"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.physa.2016.07.077","article-title":"Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals","volume":"465","author":"Azami","year":"2017","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Ali, S., Islam, M.S., Ahmad, S.A., and Escudero, J. (2016, January 4\u20138). EEG markers for early detection and characterization of vascular dementia during working memory tasks. Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2016.7843471"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Ali, S., Islam, S., Ahmad, S., and Escudero, J. (2015, January 6\u20138). EEG wavelet spectral analysis during a working memory tasks in stroke-related mild cognitive impairment patients. Proceedings of the International Conference for Innovation in Biomedical Engineering and Life Sciences, Putrajaya, Malaysia.","DOI":"10.1007\/978-981-10-0266-3_17"},{"key":"ref_70","unstructured":"Al-Qazzaz, N.K., Ali, S., Ahmad, S.A., Islam, M.S., and Escudero, J. (2016, January 22\u201323). Entropy-based markers of EEG background activity of stroke-related mild cognitive impairment and vascular dementia patients. Proceedings of the 2nd International Conference on Sensors Engineering and Electronics Instrumental Advances (SEIA 2016), Barcelona, Spain."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"59844","DOI":"10.1109\/ACCESS.2019.2914872","article-title":"Exploiting EEG signals and audiovisual feature fusion for video emotion recognition","volume":"7","author":"Xing","year":"2019","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1037\/0021-843X.111.2.302","article-title":"Crying threshold and intensity in major depressive disorder","volume":"111","author":"Rottenberg","year":"2002","journal-title":"J. Abnorm. Psychol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s11517-008-0392-1","article-title":"Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer\u2019s disease patients","volume":"46","author":"Escudero","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"237","DOI":"10.2478\/msr-2014-0032","article-title":"Spectral EEG features of a short psycho-physiological relaxation","volume":"14","author":"Teplan","year":"2014","journal-title":"Meas. Sci. Rev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jocn.2018.06.049","article-title":"EEG-based multi-feature fusion assessment for autism","volume":"56","author":"Kang","year":"2018","journal-title":"J. Clin. Neurosci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3389\/fncom.2017.00115","article-title":"Electroencephalography amplitude modulation analysis for automated affective tagging of music video clips","volume":"11","author":"Clerico","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.neunet.2007.09.020","article-title":"Enhanced automatic artifact detection based on independent component analysis and Renyi\u2019s entropy","volume":"21","author":"Mammone","year":"2008","journal-title":"Neural Netw."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1007\/s10439-011-0312-7","article-title":"Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation","volume":"39","author":"Escudero","year":"2011","journal-title":"Ann. Biomed. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1109\/TBME.2007.894968","article-title":"Artifact removal in magnetoencephalogram background activity with independent component analysis","volume":"54","author":"Escudero","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_80","unstructured":"Davidson, R.J., and Begley, S. (2012). The Emotional Life of Your Brain: How Its Unique Patterns Affect the Way You Think, Feel, and Live--and How You Can Change Them, Hachette."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1093\/schbul\/sbq046","article-title":"Temporal lobe structures and facial emotion recognition in schizophrenia patients and nonpsychotic relatives","volume":"37","author":"Goghari","year":"2010","journal-title":"Schizophr. Bull."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.socscimed.2015.09.020","article-title":"Happiness and longevity in the United States","volume":"145","author":"Lawrence","year":"2015","journal-title":"Soc. Sci. Med."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1002\/da.20686","article-title":"Depression gets old fast: Do stress and depression accelerate cell aging?","volume":"27","author":"Wolkowitz","year":"2010","journal-title":"Depress. Anxiety"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Chellappan, K., Mohsin, N.K., Ali, S.H.B.M., and Islam, M. (2012, January 17\u201319). Post-stroke Brain Memory Assessment Framework. Proceedings of the IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, Malaysia.","DOI":"10.1109\/IECBES.2012.6498190"},{"key":"ref_85","first-page":"372","article-title":"Anger and health risk behaviors","volume":"3","author":"Staicu","year":"2010","journal-title":"J. Med. Life"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/59\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:44:12Z","timestamp":1760190252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,20]]},"references-count":85,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010059"],"URL":"https:\/\/doi.org\/10.3390\/s20010059","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,20]]}}}