{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T18:50:13Z","timestamp":1792349413668,"version":"4.4.0"},"reference-count":11,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurosciences"],"abstract":"\n Background<\/jats:title>\n motion recognition using electroencephalography (EEG) has gained significant attention in the fields of affective computing and human\u2013computer interaction due to its ability to capture real-time neural responses. EEG signal variations associated with emotional states can be analysed using wavelet-based signal processing within the arousal\u2013valence framework.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n This study aims to analyse EEG signal variations associated with emotional states using a wavelet-based signal processing approach within the arousal\u2013valence framework.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A prospective observational study was conducted on 48 healthy participants. Emotional states were induced using image, video and music stimuli categorised under the arousal\u2013valence model. EEG signals were recorded using the standard 10\u201320 electrode placement system. The acquired signals were pre-processed and analysed using discrete wavelet transform (DWT) to extract time\u2013frequency features, including energy and entropy. Statistical analysis was performed to evaluate hemispheric differences and gender-based variations.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Differences in EEG activity were observed between the left and right hemispheres across multiple emotional states (p < .05). Video and music stimuli produced stronger and more distinguishable EEG responses compared to image-based stimuli. Negative emotions demonstrated higher discriminative patterns than positive emotions. Gender-based variations were also observed in EEG responses.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Wavelet-based EEG feature analysis provides meaningful and discriminative information for emotion recognition and may provide a robust feature set for future machine learning-based emotion recognition systems. Further studies incorporating classification algorithms and larger data sets are required to validate their predictive performance.<\/jats:p>\n <\/jats:sec>","DOI":"10.1177\/09727531261483432","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:13:28Z","timestamp":1792066408000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Electroencephalography Changes in Emotional Recognition: A Signal Processing Approach Using Wavelet Features"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2253-7873","authenticated-orcid":false,"given":"Beulah","family":"Snowin","sequence":"first","affiliation":[{"name":"School of Allied Health Sciences, Vels Institute of Science Technology & Advanced Studies (VISTAS), Pallavaram, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philo","family":"Hazeena","sequence":"additional","affiliation":[{"name":"Department of Neurology, Sri Ramachandra Institute of Higher Education and Research (SRIHER), Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lakshmi","family":"Narasimhan","sequence":"additional","affiliation":[{"name":"Department of Neurology, Sri Ramachandra Institute of Higher Education and Research (SRIHER), Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8875426"},{"key":"e_1_3_6_3_2","article-title":"Deep learning-based EEG emotion recognition: current trends and future perspectives","volume":"14","author":"Wang X","year":"2023","unstructured":"Wang X, , Liu Y, , Sourina O, . Deep learning-based EEG emotion recognition: current trends and future perspectives. Front Psychol 2023; 14: 1126994.","journal-title":"Front Psychol"},{"key":"e_1_3_6_4_2","article-title":"A systematic literature review of emotion recognition using EEG signals","volume":"79","author":"Prabowo DW","year":"2023","unstructured":"Prabowo DW, and Sharma LD. A systematic literature review of emotion recognition using EEG signals. Cogn Syst Res 2023; 79: 101152.","journal-title":"Cogn Syst Res"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0954579405050340"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.06.254"},{"key":"e_1_3_6_7_2","first-page":"530","volume":"2006","author":"Chanel G","unstructured":"Chanel G, , Kronegg J, , Grandjean D, . Emotion assessment: arousal evaluation using EEG and peripheral physiological signals. In: Gunsel B, Jain AK, Tekalp AM, Sankur B. (eds) Multimedia content representation, classification and security. Berlin: Springer; 2006. pp. 530\u2013537.","journal-title":"Multimedia content representation, classification and security"},{"issue":"10","key":"e_1_3_6_8_2","first-page":"355","article-title":"Emotion recognition based on EEG using LSTM recurrent neural network","volume":"8","author":"Alhagry S","year":"2017","unstructured":"Alhagry S, , Fahmy AA, , and El-Khoribi RA. Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl 2017; 8(10): 355\u2013358.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2768030"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22336-5_13"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.101027"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.03.001"}],"container-title":["Annals of Neurosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09727531261483432","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09727531261483432","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09727531261483432","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T18:16:22Z","timestamp":1792347382000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09727531261483432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,15]]},"references-count":11,"alternative-id":["10.1177\/09727531261483432"],"URL":"https:\/\/doi.org\/10.1177\/09727531261483432","relation":{},"ISSN":["0972-7531","0976-3260"],"issn-type":[{"value":"0972-7531","type":"print"},{"value":"0976-3260","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,15]]},"article-number":"09727531261483432"}}