{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T14:10:27Z","timestamp":1729174227073,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Electroencephalography (EEG), a technique that uses electrodes on the scalp to record changes in cerebral electrical potentials, plays a key role in various brain-computer interface applications. However, due to the large differences in EEG distribution across subjects, how to accurately classify cross-subject EEG is still a challenge. In practice, it is unavoidable to collect data for each subject to perform calibration, which is cumbersome and expensive. In this paper, we propose a Cross Subject EEG Prototypical Network (CS-EEGPNet) for accurate few-shot EEG classification. To the best of our knowledge, we are the first to eliminate inter-subject distribution shift in few-shot EEG classification through multiple feature layers computation. Specifically, to avoid the effect of individual differences, we propose a few-shot feature normalization (FSFN) method, which uses the mean and variance of the same subject\u2019s support set features to normalize the query set features. Meanwhile, we design a prototypical module based on auxiliary factors. This module utilizes learnable vectors to store category-related information for assisting the construction of prototypes, thus mitigating the effects of EEG\u2019s randomness and non-stationary characteristics on cross-subject few shot classification. To validate the effectiveness of the method, we selecte the motor imagery paradigm, which is relatively complex in brain-computer interfaces, and test our method on the BCIC IV 2a and HGD datasets. The results demonstrate that our method significantly enhances EEG classification performance, achieving the state-of-the-art accuracies.<\/jats:p>","DOI":"10.3233\/faia240821","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:28:17Z","timestamp":1729171697000},"source":"Crossref","is-referenced-by-count":0,"title":["A Study of Prototypical Network Techniques for Cross-Subject EEG Analysis"],"prefix":"10.3233","author":[{"given":"Wenchao","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China"},{"name":"Faculty of Computing Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guagnyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Computing Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Computing Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjian","family":"Bo","sequence":"additional","affiliation":[{"name":"Shenzhen Academy of Aerospace Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Computing Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China"},{"name":"Faculty of Computing Harbin Institute of Technology, Harbin, China"},{"name":"Shenzhen Academy of Aerospace Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240821","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:28:17Z","timestamp":1729171697000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240821"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240821","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}