{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:58:28Z","timestamp":1785164308783,"version":"3.55.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32161143022"],"award-info":[{"award-number":["32161143022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the \u03b1 frequency band could effectively represent negative emotions, while the \u03b1 frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2023.1200701","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T04:54:57Z","timestamp":1689051297000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Emotional brain network decoded by biological spiking neural network"],"prefix":"10.3389","volume":"17","author":[{"given":"Hubo","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongguang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Awuti","family":"Abudusalamu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxue","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.neucom.11078","article-title":"EasyMKL: a scalable multiple kernel learning algorithm,","volume":"169","author":"Aiolli","year":"2015","journal-title":"Neurocomputing"},{"key":"B2","doi-asserted-by":"publisher","first-page":"191080","DOI":"10.1109\/ACCESS.2020.3032380","article-title":"Discrimination of genuine and acted emotional expressions using EEG signal and machine learning, IEEE","volume":"8","author":"Alex","year":"2020","journal-title":"Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/computers9040095","article-title":"Machine-learning-based emotion recognition system using EEG signals","volume":"9","author":"Alhalaseh","year":"2020","journal-title":"Computers"},{"key":"B4","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.jneumeth.10018","article-title":"The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference","volume":"223","author":"Barnett","year":"2014","journal-title":"J. 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