Multiclass Classification of EEG Motor Imagery Signals Based on Transfer Learning

研究成果: 書貢獻/報告類型會議論文篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Multiclass Classification of EEG signal is essential for brain computer interface (BCI) applications but extremely time consuming. We proposed a subject-weighted adaptive transfer learning method in conjunction with MLP and CNN classifiers for fast classification of multiclass EEG dataset.Analytic results show that CNN generally outperforms MLP in this multi-class classification. The use of transfer learning is efficient for building the predictive model without decreasing the accuracy and 2D CNN is more robust to between-subject variabilities.

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主出版物標題Proceedings of the 2022 8th International Conference on Applied System Innovation, ICASI 2022
編輯Shoou-Jinn Chang, Sheng-Joue Young, Artde Donald Kin-Tak Lam, Liang-Wen Ji, Stephen D. Prior
發行者Institute of Electrical and Electronics Engineers Inc.
頁面140-143
頁數4
ISBN(電子)9781665496506
DOIs
出版狀態已出版 - 2022
事件8th International Conference on Applied System Innovation, ICASI 2022 - Nantou, Taiwan
持續時間: 21 4月 202223 4月 2022

出版系列

名字Proceedings of the 2022 8th International Conference on Applied System Innovation, ICASI 2022

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???event.eventtypes.event.conference???8th International Conference on Applied System Innovation, ICASI 2022
國家/地區Taiwan
城市Nantou
期間21/04/2223/04/22

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