{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:24Z","timestamp":1747216164246,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685489"}],"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>The brain-computer interface (BCI) is a promising technology that could bring about a significant revolution in various fields, including healthcare and human enhancement. One commonly used BCI method in healthcare, particularly in rehabilitation, is the analysis of motor imagery (MI) through an electroencephalogram (EEG). Our study introduces a hybrid deep learning model called CNN-Transformer-LSTM, which utilizes multi-channel EEG signals to classify MI binary and multiclass automatically. Our experiments have shown that this proposed method is more effective than previous state-of-the-art studies at accurately classifying MI using multi-channel EEG signals.<\/jats:p>","DOI":"10.3233\/faia241048","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:59:39Z","timestamp":1729173579000},"source":"Crossref","is-referenced-by-count":2,"title":["Automatic Motor Imagery Classification by CNN-Transformer-LSTM Using Multi-Channel EEG Signals"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3037-5298","authenticated-orcid":false,"family":"Duc Thien Pham","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of West Bohemia, Pilsen, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4665-8946","authenticated-orcid":false,"given":"Roman","family":"Mou\u010dek","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of West Bohemia, Pilsen, Czech Republic"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241048","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:59:39Z","timestamp":1729173579000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241048","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}