{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:10:08Z","timestamp":1779383408677,"version":"3.53.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore Ministry of Education (MOE)","award":["PG 03\/21 YR"],"award-info":[{"award-number":["PG 03\/21 YR"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.<\/jats:p>","DOI":"10.3390\/s23020915","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:57:33Z","timestamp":1673578653000},"page":"915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4526-0749","authenticated-orcid":false,"given":"Rajamanickam","family":"Yuvaraj","sequence":"first","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9023-297X","authenticated-orcid":false,"given":"Prasanth","family":"Thagavel","sequence":"additional","affiliation":[{"name":"Interdisciplinary Graduate School, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0144-3746","authenticated-orcid":false,"given":"John","family":"Thomas","sequence":"additional","affiliation":[{"name":"Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6210-1067","authenticated-orcid":false,"given":"Jack","family":"Fogarty","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6773-893X","authenticated-orcid":false,"given":"Farhan","family":"Ali","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A Review of Emotion Recognition Using Physiological Signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","article-title":"Emotion recognition from physiological signal analysis: A review","volume":"343","author":"Egger","year":"2019","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ko, B.C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18.","DOI":"10.3390\/s18020401"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47795","DOI":"10.1109\/ACCESS.2021.3068045","article-title":"A comprehensive review of speech emotion recognition systems","volume":"9","author":"Wani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"758212","DOI":"10.3389\/fncom.2021.758212","article-title":"Review on emotion recognition based on electroencephalography","volume":"15","author":"Liu","year":"2021","journal-title":"Front. Comput. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schaaff, K., and Schultz, T. (2009, January 10\u201312). Towards emotion recognition from electroencephalographic signals. Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009), Amsterdam, The Netherlands.","DOI":"10.1109\/ACII.2009.5349316"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/T-AFFC.2010.7","article-title":"Emotion recogntion from brain signals using hybrid adaptive filtering and higher order crossings analysis","volume":"1","author":"Petrantonakis","year":"2010","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/TITB.2010.2041553","article-title":"Toward emotion aware computing:An integrated approach using multichannel neurophysiological recordings and affective visual stimuli","volume":"14","author":"Frantzidis","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1109\/TBME.2012.2217495","article-title":"Toward an EEG-based recognition of music liking using time-frequency analysis","volume":"59","author":"Hadjidimitriou","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature extraction and selection for emotion recogntion from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Sourina, O. (2014). Real-time subject-dependent eeg-based emotion recognition algorithm. Transactions on Computational Science XXIII, Springer.","DOI":"10.1109\/SMC.2014.6974415"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.ijpsycho.2014.07.014","article-title":"Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinsosn\u2019s disease","volume":"94","author":"Yuvaraj","year":"2014","journal-title":"Int. J. Psychophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.bbe.2020.04.005","article-title":"Comparison of different feature extraction methods for EEG-based emotion recognition","volume":"40","author":"Nawaz","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_14","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_15","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. Health Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A Multimodal Database for Affect Recognition and Implicit Tagging","volume":"3","author":"Soleymani","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_17","first-page":"479","article-title":"AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups","volume":"12","author":"Abadi","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_18","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_19","first-page":"1441","article-title":"Emotion recognition based on EEG feature maps through deep learning network","volume":"24","author":"Topic","year":"2021","journal-title":"Int. J Eng. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fnins.2018.00162","article-title":"Exploring EEG features in cross-subject emotion recognition","volume":"12","author":"Li","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TCDS.2018.2826840","article-title":"Domain adaptation techniques for EEG-based emotion recognition: A comparative study on two public datasets","volume":"11","author":"Lan","year":"2018","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1017\/S0954579405050340","article-title":"The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology","volume":"17","author":"Posner","year":"2005","journal-title":"Dev. Psychopathol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/cpe.4446","article-title":"Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction","volume":"30","author":"Liu","year":"2018","journal-title":"Concurr. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TAFFC.2018.2840973","article-title":"A mutual information based adaptive windowing of informative EEG for emotion recognition","volume":"11","author":"Piho","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Garg, A., Kapoor, A., Bedi, A.K., and Sunkaria, R.K. (2019, January 26\u201328). Merged LSTM Model for emotion classification using EEG signals. Proceedings of the International Conference on Data Science and Engineering (ICDSE), Patna, India.","DOI":"10.1109\/ICDSE47409.2019.8971484"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Putra, A.E., Atmaji, C., and Ghaleb, F. (2018, January 7\u20138). EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor. Proceedings of the 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia.","DOI":"10.1109\/ICSTC.2018.8528652"},{"key":"ref_27","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":"2013","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.cmpb.2019.03.015","article-title":"Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method","volume":"173","author":"Taran","year":"2019","journal-title":"Comput. Programs Biomed."},{"key":"ref_29","unstructured":"Bajaj, V., and Pachori, R.B. (June, January 30). Human Emotion Classification from EEG Signals Using Multiwavelet Transform. Proceedings of the International Conference on Medical Biometrics, Shenzhen, China."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Avramidis, K., Zlatintsi, A., Garoufis, C., and Maragos, P. (2022, January 20). Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition. Available online: https:\/\/arxiv.org\/abs\/2010.16310.","DOI":"10.23919\/EUSIPCO54536.2021.9616140"},{"key":"ref_31","unstructured":"Gavrilova, M.L., Tan, C.J.K., and Kuijper, A. (2013). Real-Time Fractal-Based Valence Level Recognition from EEG. Transactions on Computational Science XVIII, Springer. Lecture Notes in Computer Science."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0010-4825(88)90041-8","article-title":"Fractals and the analysis of waveforms","volume":"18","author":"Katz","year":"1998","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hatamikia, S., and Nasrabadi, A.M. (2014, January 26\u201328). Recognition of emotional states induced by music videos based on nonlinear feature extraction and SOM classification. Proceedings of the 21st Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran.","DOI":"10.1109\/ICBME.2014.7043946"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0167-2789(88)90081-4","article-title":"Approach to an irregular time series on the basis of the fractal theory","volume":"31","author":"Higuchi","year":"1988","journal-title":"Phys. D"},{"key":"ref_35","first-page":"1","article-title":"A Review on nonlinear methods using electroencephalographic recordings for emotion recognition","volume":"10","author":"Alcaraz","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14797","DOI":"10.1109\/ACCESS.2017.2724555","article-title":"Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors","volume":"5","author":"Mehmood","year":"2017","journal-title":"IEEE Access."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_38","first-page":"312","article-title":"The physical significance of time domain descriptors in EEG analysis. Electroencephalogr","volume":"34","author":"Hjorth","year":"1973","journal-title":"Clin. Neurophysiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.5815\/ijigsp.2012.01.03","article-title":"Classification of brain activity in emotional states using HOS analysis","volume":"1","author":"Hosseini","year":"2012","journal-title":"Int. J Image Graph. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1007\/s00521-016-2756-z","article-title":"A novel Parkinson\u2019s Disease Diagnosis Index using higher-order spectra features in EEG signals","volume":"30","author":"Yuvaraj","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1080\/17415977.2020.1797716","article-title":"Parameter selection of Gaussian kernel SVM based on local density of training set","volume":"29","author":"Yang","year":"2021","journal-title":"Inverse. Probl. Sci. Eng."},{"key":"ref_42","unstructured":"Mehmood, R.M., and Lee, H.J. (July, January 29). Emotion classification of EEG brain signal using SVM and KNN. Proceedings of the International Conference on Multimedia & Expo Workshops (ICMEW), Turin, Italy."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Satyanarayana, K.N.V., Shankar, T., Poojita, G., Vinay, G., Amaranadh, H.N.S.V.l.S., and Babu, A.G. (2022, January 29\u201331). An Approach to EEG based Emotion Identification by SVM classifier. Proceedings of the 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC53470.2022.9753699"},{"key":"ref_44","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Elsevier."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/978-3-642-22336-5_13","article-title":"Real time EEG-based emotion recognition and its applications","volume":"6670","author":"Liu","year":"2011","journal-title":"Trans. Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00371-015-1183-y","article-title":"Real-time EEGbased emotion monitoring using stable features","volume":"32","author":"Lan","year":"2016","journal-title":"Visual Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2050030","DOI":"10.1142\/S0129065720500306","article-title":"Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks","volume":"30","author":"Thomas","year":"2020","journal-title":"Int. J. Neural Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jukic, S., Saracevic, M., Subasi, A., and Kevric, J. (2020). Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals. Mathematics, 8.","DOI":"10.3390\/math8091481"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biopsycho.2018.06.008","article-title":"Fractal dimesion of EEG signals and heart dynamics in discrete emotional states","volume":"137","year":"2018","journal-title":"Biol. Psychol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zappasodi, F., Olejarczyk, E., Marzetti, L., Assenza, G., Pizzella, V., and Tecchio, F. (2014). Fractal dimension of EEG activity senses neuronal impairment in acute stroke. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0100199"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3218","DOI":"10.1162\/jocn_a_00024","article-title":"Inflexibly focused under stress: Acute psychosocial stress increases shielding of action goals at the expense of reduced cognitive flexibility with increasing time lag to the stressor","volume":"23","author":"Plessow","year":"2011","journal-title":"J. Cogn. Neurosci."},{"key":"ref_52","unstructured":"Siddharth, S., Jung, T.-P., and Sejnowski, T.J. (2019). Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1109\/TBME.2019.2897651","article-title":"EEG based emotion recognition by combining functional connectivity network and local activations","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106243","DOI":"10.1016\/j.knosys.2020.106243","article-title":"EEG-based Emotion Recognition using an End-to-End Regional-Asymmetric Convolutional Neural Network","volume":"205","author":"Cui","year":"2020","journal-title":"Knowl. Based. Systs."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"942979","DOI":"10.3389\/fncom.2022.942979","article-title":"E2ENNet: An End-to-End Neural Network for Emotional Brain-Computer Interface","volume":"16","author":"Han","year":"2022","journal-title":"Font. Comput. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/915\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:42Z","timestamp":1760119482000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020915"],"URL":"https:\/\/doi.org\/10.3390\/s23020915","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]}}}