{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:35:04Z","timestamp":1775421304532,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, relaxed), happy (excited, happy, pleased), sad (sleepy, bored, sad). A total 120 emotion signals were collected by using Emotive 14 channel EEG headset. Emotions are elicited by using three types of stimulus thoughts, audio and video. The system is trained by using captured database of emotion signals which include 30 signals of each emotion class. A total of 24 features were extracted by performing Chirplet transform. Band power is ranked as the prominent feature. The multimodel approach of classifier is used to classify emotions. Classification accuracy is tested for K-nearest neighbor (KNN), convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN) classifiers. The system is tested to detect emotions of intellectually disable people. Meditation music therapy is used to stable mental state. It is found that it changed emotions of both intellectually disabled and normal participants from the annoying state to the relaxed state. A 75% positive transformation of mental state is obtained in the participants by using music therapy. This research study presents a novel approach for detailed analysis of brain EEG signals for emotion detection and stabilize mental state.<\/jats:p>","DOI":"10.3390\/computers10030037","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T11:18:38Z","timestamp":1616152718000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-9876","authenticated-orcid":false,"given":"Nisha Vishnupant","family":"Kimmatkar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India"}]},{"given":"B. Vijaya","family":"Babu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh 522502, India"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1049\/iet-wss.2014.0056","article-title":"Real time emotion detection within a wireless sensor network and its impact on power consumption","volume":"4","author":"Matiko","year":"2014","journal-title":"IET Wirel. Sens. Syst."},{"key":"ref_2","unstructured":"Kimmatkar, N.V., and Vijaya Babu, B. (2017). A Survey and Comparative Analysis of Various Existing Techniques Used to Develop an Intelligent Emotion Recognition System Using EEG Signal Analysis, Serials Publications Pvt. Ltd."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bhardwaj, A., Gupta, A., Jain, P., Rani, A., and Yadav, J. (2015, January 19\u201320). Classification of human emotions from EEG signals using SVM and LDA Classifiers. Proceedings of the 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2015.7095376"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gawali, B.W., Rao, S., Abhang, P., Rokade, P., and Mehrotra, S.C. (2012, January 19\u201320). Classification of EEG signals for different emotional states. Proceedings of the Fourth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom2012), Bangalore, India.","DOI":"10.1049\/cp.2012.2521"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Blaiech, H., Neji, M., Wali, A., and Alimi, A.M. (2013, January 4\u20136). Emotion recognition by analysis of EEG signals. Proceedings of the 13th International Conference on Hybrid Intelligent Systems (HIS 2013), Gammarth, Tunisia.","DOI":"10.1109\/HIS.2013.6920451"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kaundanya, V.L., Patil, A., and Panat, A. (2015, January 2\u20134). Performance of k-NN classifier for emotion detection using EEG signals. Proceedings of the 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2015.7322687"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Murugappan, M. (2011, January 28\u201329). Human emotion classification using wavelet transform and KNN. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligence Robotics, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICPAIR.2011.5976886"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Mehmood, R.M., and Lee, H.J. (July, January 29). Emotion classification of EEG brain signal using SVM and KNN. Proceedings of the 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, Italy.","DOI":"10.1109\/ICMEW.2015.7169786"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., and Lu, B.L. (2013, January 6\u20138). Differential entropy feature for EEG-based emotion classification. Proceedings of the 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA.","DOI":"10.1109\/NER.2013.6695876"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Harischandra, J., and Perera, M.U.S. (2012, January 17\u201319). Intelligent emotion recognition system using brain signals (EEG). Proceedings of the 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, Malaysia.","DOI":"10.1109\/IECBES.2012.6498050"},{"key":"ref_13","unstructured":"Fan, J., Wade, J.W., Bian, D., Key, A.P., Warren, Z.E., Mion, L.C., and Sarkar, N. (2015, January 25\u201329). A Step towards EEG-based brain computer interface for autism intervention. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy."},{"key":"ref_14","unstructured":"bin Yunus, J. (2012, January 12\u201314). The effect of noise removing on emotional classification. Proceedings of the 2012 International Conference on Computer & Information Science (ICCIS), Kuala Lumpur, Malaysia."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ramaraju, S., Izzidien, A., and Roula, M.A. (2015, January 25\u201329). The detection and classification of the mental state elicited by humor from EEG patterns. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318648"},{"key":"ref_16","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":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_17","unstructured":"Ekanayake, H. (2020, April 28). P300 and Emotiv EPOC: DoesEmotiv EPOC Capture Real EEG?. Available online: http:\/\/neurofeedback.visaduma.info\/emotivresearch.htm."},{"key":"ref_18","unstructured":"EMOTIV (2020, June 12). Brain Controlled Technology Using Emotiv\u2019s Algorithms |EMOTIV [Online]. Available online: https:\/\/www.emotiv.com\/brain-controlled-technology\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1016\/j.ymssp.2015.09.004","article-title":"General linear chirplet transform","volume":"70","author":"Yu","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","unstructured":"(2020, May 12). Feature Selection for Machine Learning in Python. Available online: https:\/\/machinelearningmastery.com\/feature-selection-machine-learning-python\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Caesarendra, W., Tjahjowidodo, T., and Pamungkas, D. (2017, January 23\u201325). EMG based classification of hand gestures using PCA and ANFIS. Proceedings of the 2017 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics), Bali, Indonesia.","DOI":"10.1109\/ROBIONETICS.2017.8203430"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kimmatkar, N.V., and Babu, B.V. (2017, January 11\u201312). Initial analysis of brain EEG signal for mental state detection of human being. Proceedings of the 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI.2017.8300934"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kimmatkar, N.V., and Babu, V.B. (2018, January 11\u201314). Human Emotion Classification from Brain EEG Signal Using Multimodal Approach of Classifier. Proceedings of the 2018 International Conference on Intelligent Information Technology, Chennai, India.","DOI":"10.1145\/3193063.3193067"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/3\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:38:12Z","timestamp":1760161092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/3\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":23,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["computers10030037"],"URL":"https:\/\/doi.org\/10.3390\/computers10030037","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}