{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T09:19:26Z","timestamp":1777886366539,"version":"3.51.4"},"reference-count":78,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University Synergy Innovation Program of Anhui Province","award":["No. GXXT-2021-044"],"award-info":[{"award-number":["No. GXXT-2021-044"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["No. KJ2021A0412"],"award-info":[{"award-number":["No. KJ2021A0412"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["No. KJ2019A0068"],"award-info":[{"award-number":["No. KJ2019A0068"]}]},{"name":"Natural Science Key Foundation of Anhui Provincial Education Department of China","award":["No. GXXT-2021-044"],"award-info":[{"award-number":["No. GXXT-2021-044"]}]},{"name":"Natural Science Key Foundation of Anhui Provincial Education Department of China","award":["No. KJ2021A0412"],"award-info":[{"award-number":["No. KJ2021A0412"]}]},{"name":"Natural Science Key Foundation of Anhui Provincial Education Department of China","award":["No. KJ2019A0068"],"award-info":[{"award-number":["No. KJ2019A0068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The dynamic of music is an important factor to arouse emotional experience, but current research mainly uses short-term artificial stimulus materials, which cannot effectively awaken complex emotions and reflect their dynamic brain response. In this paper, we used three long-term stimulus materials with many dynamic emotions inside: the \u201cWaltz No. 2\u201d containing pleasure and excitement, the \u201cNo. 14 Couplets\u201d containing excitement, briskness, and nervousness, and the first movement of \u201cSymphony No. 5 in C minor\u201d containing passion, relaxation, cheerfulness, and nervousness. Approximate entropy (ApEn) and sample entropy (SampEn) were applied to extract the non-linear features of electroencephalogram (EEG) signals under long-term dynamic stimulation, and the K-Nearest Neighbor (KNN) method was used to recognize emotions. Further, a supervised feature vector dimensionality reduction method was proposed. Firstly, the optimal channel set for each subject was obtained by using a particle swarm optimization (PSO) algorithm, and then the number of times to select each channel in the optimal channel set of all subjects was counted. If the number was greater than or equal to the threshold, it was a common channel suitable for all subjects. The recognition results based on the optimal channel set demonstrated that each accuracy of two categories of emotions based on \u201cWaltz No. 2\u201d and three categories of emotions based on \u201cNo. 14 Couplets\u201d was generally above 80%, respectively, and the recognition accuracy of four categories based on the first movement of \u201cSymphony No. 5 in C minor\u201d was about 70%. The recognition accuracy based on the common channel set was about 10% lower than that based on the optimal channel set, but not much different from that based on the whole channel set. This result suggested that the common channel could basically reflect the universal features of the whole subjects while realizing feature dimension reduction. The common channels were mainly distributed in the frontal lobe, central region, parietal lobe, occipital lobe, and temporal lobe. The channel number distributed in the frontal lobe was greater than the ones in other regions, indicating that the frontal lobe was the main emotional response region. Brain region topographic map based on the common channel set showed that there were differences in entropy intensity between different brain regions of the same emotion and the same brain region of different emotions. The number of times to select each channel in the optimal channel set of all 30 subjects showed that the principal component channels representing five brain regions were Fp1\/F3 in the frontal lobe, CP5 in the central region, Pz in the parietal lobe, O2 in the occipital lobe, and T8 in the temporal lobe, respectively.<\/jats:p>","DOI":"10.3390\/e24121735","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:38:49Z","timestamp":1669621129000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG"],"prefix":"10.3390","volume":"24","author":[{"given":"Zun","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Arts and Design, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Arts and Design, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songjie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44317","DOI":"10.1109\/ACCESS.2019.2908285","article-title":"Accurate EEG-based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1109\/JSEN.2019.2958210","article-title":"A Single-channel Consumer-grade EEG Device for Brain- computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation","volume":"20","author":"Autthasan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3996","DOI":"10.1109\/JSEN.2019.2962874","article-title":"Consumer grade EEG Measuring Sensors as Research Tools: A review","volume":"20","author":"Sawangjai","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94601","DOI":"10.1109\/ACCESS.2021.3091487","article-title":"Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques","volume":"9","author":"Islam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijpsycho.2005.04.007","article-title":"From Emotion Perception to Emotion Experience: Emotions Evoked by Pictures and Classical Music","volume":"60","author":"Baumgartner","year":"2006","journal-title":"Int. J. Psychophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"139332","DOI":"10.1109\/ACCESS.2020.3011882","article-title":"Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals","volume":"8","author":"Sheykhivand","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.26599\/BSA.2020.9050026","article-title":"Video-triggered EEG-emotion Public Databases and Current Methods: A survey","volume":"6","author":"Hu","year":"2020","journal-title":"Brain Sci. Adv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12177","DOI":"10.1109\/ACCESS.2019.2891579","article-title":"MPED: A Multi-modal Physiological Emotion Database for Discrete Emotion Recognition","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103349","DOI":"10.1016\/j.bspc.2021.103349","article-title":"EEG-based Emotion Recognition in an Immersive Virtual Reality Environment: From Local Activity to Brain Network Features","volume":"72","author":"Yu","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Suhaimi, N.S., Mountstephens, J., and Teo, J. (2022). A Dataset for Emotion Recognition Using Virtual Reality and EEG (DER-VREEG): Emotional State Classification Using Low-Cost Wearable VR-EEG Headsets. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6010016"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1017\/S0140525X08005293","article-title":"Emotional Responses to Music: The Need to Consider Underlying Mechanisms","volume":"31","author":"Juslin","year":"2008","journal-title":"Behav. Brain Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1037\/a0013505","article-title":"An Experience Sampling Study of Emotional Reactions to Music: Listener, Music, and Situation","volume":"8","author":"Juslin","year":"2008","journal-title":"Emotion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/nrn3666","article-title":"Brain Correlates of Music-evoked Emotions","volume":"15","author":"Koelsch","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.neuron.2018.04.029","article-title":"Investigating the Neural Encoding of Emotion with Music","volume":"98","author":"Koelsch","year":"2018","journal-title":"Neuron"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9415","DOI":"10.1038\/s41598-019-45105-2","article-title":"Electroencephalography Reflects the Activity of Sub-cortical Brain Regions during Approach-withdrawal Behaviour While Listening to Music","volume":"9","author":"Daly","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s10548-012-0227-0","article-title":"Emotions, Arousal, and Frontal Alpha Rhythm Asymmetry During Beethoven\u2019s 5th Symphony","volume":"25","author":"Mikutta","year":"2012","journal-title":"Brain Topogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"502","DOI":"10.4028\/www.scientific.net\/AMM.311.502","article-title":"Effect of Music Listening on Frontal EEG Asymmetry","volume":"311","author":"Lee","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neulet.2009.05.068","article-title":"Resting Frontal EEG Alpha-asymmetry Predicts the Evaluation of Affective Musical Stimuli","volume":"460","author":"Schmidt","year":"2009","journal-title":"Neurosci. Lett."},{"key":"ref_19","first-page":"115","article-title":"Indian Classical Music with Incremental Variation in Tempo and Octave Promotes Better Anxiety Reduction and Controlled Mind Wandering-A Randomised Controlled EEG Study","volume":"17","author":"Sharma","year":"2021","journal-title":"Explor. J. Sci. Heal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/02699930126048","article-title":"Frontal Brain Electrical Activity (EEG) Distinguishes Valence and Intensity of Musical Emotions","volume":"15","author":"Schmidt","year":"2001","journal-title":"Cogn. Emot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1111\/j.1749-6632.2001.tb05764.x","article-title":"Frontal EEG Responses as a Function of Affective Musical Features","volume":"930","author":"Tsang","year":"2001","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1111\/j.1469-8986.2007.00497.x","article-title":"Music and Emotion: Electrophysiological Correlates of the Processing of Pleasant and Unpleasant Music","volume":"44","author":"Sammler","year":"2007","journal-title":"Psychophysiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"261","DOI":"10.4236\/psych.2013.43A039","article-title":"Approach the Good, withdraw from the Bad\u2014A Review on Frontal Alpha Asymmetry Measures in Applied Psychological Research","volume":"4","author":"Briesemeister","year":"2013","journal-title":"Psychology"},{"key":"ref_24","first-page":"1","article-title":"Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals","volume":"2019","author":"Hou","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102251","DOI":"10.1016\/j.bspc.2020.102251","article-title":"Influence of Music Liking on EEG Based Emotion Recognition","volume":"64","author":"Daimi","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.bspc.2018.01.015","article-title":"Music Induced Emotion Using Wavelet Packet Decomposition\u2014An EEG Study","volume":"42","author":"Balasubramanian","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.chb.2016.08.029","article-title":"Human Emotion Recognition and Analysis in Response to Audio Music Using Brain Signals","volume":"65","author":"Bhatti","year":"2016","journal-title":"Comput. Hum. Behav."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TCBB.2016.2616395","article-title":"Attention Recognition in EEG-based Affective Learning Research Using CFS + KNN Algorithm","volume":"15","author":"Hu","year":"2016","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.sna.2017.07.012","article-title":"A Real-time Wearable Emotion Detection Headband Based on EEG Measurement","volume":"263","author":"Wei","year":"2017","journal-title":"Sens. Actuators A Phys."},{"key":"ref_30","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_31","unstructured":"Ahmed, T., Islam, M., and Ahmad, M. Human Emotion Modeling Based on Salient Global Features of EEG Signal. Proceedings of the 2nd International Conference on Advances in Electrical Engineering."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1063\/1.166092","article-title":"Approximate entropy (ApEn) as a complexity measure","volume":"5","author":"Pincus","year":"1995","journal-title":"Chaos"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.compeleceng.2018.09.022","article-title":"Emotion Recognition Using Empirical Mode Decomposition and Approximation Entropy","volume":"72","author":"Chen","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological Time-series Analysis Using Approximate Entropy and Sample Entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3123218","article-title":"A Low-cost Implementation of Sample Entropy in Wearable Embedded Systems: An Example of Online Analysis for Sleep EEG","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10877-012-9419-0","article-title":"Parameter Selection in Permutation Entropy for an Electroencephalographic Measure of Isoflurane Anesthetic Drug Effect","volume":"27","author":"Li","year":"2013","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of Surface EMG Signal Based on Fuzzy Entropy","volume":"15","author":"Chen","year":"2007","journal-title":"Neural Syst. Rehabil. Eng. IEEE Trans."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1097\/01.anes.0000291444.68894.ee","article-title":"Quantification of Epileptiform Electroencephalographic Activity during Sevoflurane Mask Induction","volume":"107","author":"Ermes","year":"2007","journal-title":"Anesthesiology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1016\/j.clinph.2008.08.006","article-title":"Analysis of Depth of Anesthesia with Hilbert\u2013Huang Spectral Entropy","volume":"119","author":"Li","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zuo, X., Zhang, C., H\u00e4m\u00e4l\u00e4inen, T., Gao, H.-B., Fu, Y., and Cong, F.-Y. (2022). Cross-subject Emotion Recognition Using Fused Entropy Features of EEG. Entropy, 24.","DOI":"10.3390\/e24091281"},{"key":"ref_43","first-page":"16","article-title":"EEG Entropy Measures in Anesthesia","volume":"9","author":"Liang","year":"2015","journal-title":"Front. Comput. Neuroence"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TCDS.2016.2587290","article-title":"Multichannel EEG-based Emotion Recognition Via Group Sparse Canonical Correlation Analysis","volume":"9","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.eij.2019.10.002","article-title":"Employing PCA and t-statistical Approach for Feature Extraction and Classification of Emotion from Multichannel EEG Signal","volume":"21","author":"Rahman","year":"2020","journal-title":"Egypt. Inform. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1037\/1528-3542.8.4.494","article-title":"Emotions Evoked by the Sound of Music: Characterization, Classification, and Measurement","volume":"8","author":"Zenter","year":"2008","journal-title":"Emotion"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.chb.2016.01.005","article-title":"Toward Automatic Detection of Brain Responses to Emotional Music through Analysis of EEG Effective Connectivity","volume":"58","author":"Shahabi","year":"2016","journal-title":"Comput. Hum. Behav."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Yohanes, R.E.J., Ser, W., and Huang, G.-B. (September, January 28). Discrete Wavelet Transform Coefficients for Emotion Recognition from EEG Signals. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346410"},{"key":"ref_51","unstructured":"Murugappan, M., Rizon, M., Nagarajan, R., and Yaacob, S. (2008, January 6\u20138). EEG Feature Extraction for Classifying Emotions Using FCM and FKM. Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science, Hangzhou, China."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sohaib, A.T., Qureshi, S., Hagelb\u00e4ck, J., Hilborn, O., and Jer\u010di\u0107, P. (2013). Evaluating Classifiers for Emotion Recognition Using EEG. International Conference on Augmented Cognition, Springer.","DOI":"10.1007\/978-3-642-39454-6_53"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Wen, Z.Y., Xu, R.F., and Du, J.C. (2017, January 15\u201317). A Novel Convolutional Neural Networks for Emotion Recognition Based on EEG Signal. Proceedings of the 2017 International Conference on Security, Pattern Analysis, and Cybernetics, Shenzhen, China.","DOI":"10.1109\/SPAC.2017.8304360"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neucom.2021.03.105","article-title":"Differences First in Asymmetric Brain: A Bi-hemisphere Discrepancy Convolutional Neural Network for EEG Emotion Recognition","volume":"448","author":"Huang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Thammasan, N., Fukui, K.I., and Numao, M. (2016). Application of Deep Belief Networks in EEG-based Dynamic Music-emotion Recognition. 2016 International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN.2016.7727292"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3389\/fnsys.2020.00043","article-title":"EEG-based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder","volume":"14","author":"Liu","year":"2020","journal-title":"Front. Syst. Neurosci."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/00048400902941257","article-title":"A Simulation Theory of Musical Expressivity","volume":"88","author":"Cochrane","year":"2010","journal-title":"Australas. J. Philos."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"116330","DOI":"10.1016\/j.neuroimage.2019.116330","article-title":"Nature Abhors a Paywall: How Open Science Can Realize the Potential of Naturalistic Stimuli","volume":"216","author":"Hanke","year":"2020","journal-title":"Neuroimage"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.neuroimage.2013.08.032","article-title":"The Emotion\u2013action Link? Naturalistic Emotional Stimuli Preferentially Activate the Human Dorsal Visual Stream","volume":"84","author":"Goldberg","year":"2014","journal-title":"Neuroimage"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.cogsys.2018.09.019","article-title":"Summarization of Videos by Analyzing Affective State of the User through Crowdsource","volume":"52","author":"Singhal","year":"2018","journal-title":"Cogn. Syst. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/j.procs.2018.05.087","article-title":"EEG Based Emotion Classification Mechanism in BCI","volume":"132","author":"Kaur","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/TAFFC.2017.2660485","article-title":"Real-time Movie-induced Discrete Emotion Recognition from EEG Signals","volume":"9","author":"Liu","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"Emotion Meter: A Multimodal Framework for Recognizing Human Emotions","volume":"49","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_66","unstructured":"Adams, B.L. (1994). The Effect of Visual\/aural Conditions on the Emotional Response to Music, The Florida State University."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"240","DOI":"10.2307\/3345597","article-title":"Effects of Music with Video on Responses of Nonmusic Majors: An Exploratory Study","volume":"44","author":"Geringer","year":"1996","journal-title":"J. Res. Music Educ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","article-title":"Automated EEG Analysis of Epilepsy: A Review","volume":"45","author":"Acharya","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5405\/jmbe.710","article-title":"Combining Spatial Filtering and Wavelet Transform for Classifying Human Emotions Using EEG Signals","volume":"31","author":"Murugappan","year":"2011","journal-title":"J. Med. Biol. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"30","DOI":"10.5815\/ijigsp.2011.05.05","article-title":"Emotion Recognition Method Using Entropy Analysis of EEG Signals","volume":"3","author":"Hosseini","year":"2011","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.3233\/BME-130919","article-title":"Emotion Recognition Based on the Sample Entropy of EEG","volume":"24","author":"Jie","year":"2014","journal-title":"Bio-Mediical Mater. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Shi, Y., and Eberhart, R.C. (1999). Empirical Study of Particle Swarm Optimization. Proceedings of the 1999 Congress on Evolutionary Computation, CEC99 (Cat. No. 99TH8406), IEEE.","DOI":"10.1109\/CEC.1999.785511"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Murugappan, M., Nagarajan, R., and Yaacob, S. (2009). Comparison of Different Wavelet Features from EEG Signals for Classifying Human Emotions. 2009 IEEE Symposium on Industrial Electronics & Applications, IEEE.","DOI":"10.1109\/ISIEA.2009.5356339"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/T-AFFC.2010.7","article-title":"Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis","volume":"1","author":"Panagiotis","year":"2010","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.tics.2007.06.002","article-title":"Decoding Human Brain Activity during Real-world Experiences","volume":"11","author":"Spiers","year":"2007","journal-title":"Trends Cogn. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1149\/10701.5105ecst","article-title":"A Review on Emotion Recognition with Machine Learning Using EEG Signals","volume":"107","author":"Islam","year":"2022","journal-title":"ECS Trans."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8875426","article-title":"EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities","volume":"2020","author":"Suhaimi","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_78","first-page":"8537000","article-title":"Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs","volume":"2021","author":"Sabir","year":"2021","journal-title":"J. Healthc. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1735\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:28:28Z","timestamp":1760146108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/12\/1735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,28]]},"references-count":78,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["e24121735"],"URL":"https:\/\/doi.org\/10.3390\/e24121735","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,28]]}}}