A Hybrid EEG-fNIRS Brain-Computer Interface Based on Dynamic Functional Connectivity and Long Short-Term Memory

Peng Wang, Jing He, Wenli Lan, Hui Yang, Yue Leng, Ruimin Wang, Keiji Iramina, Sheng Ge

研究成果: Contribution to journalConference article査読

抄録

In this study, we combined dynamic functional connectivity (dFC) and long short-term memory (LSTM) to obtain robust high classification accuracy on bimodal motor imagery signals. To characterize the information flow in brain cortical activity, we measured the variation of the functional interaction between different signal channels with dFC in the feature extraction stage. The variation measured by dFC was then decoded by LSTM for classification. In addition, we used the electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) bimodal signals acquired simultaneously to overcome the artifact susceptibility of EEG. The average classification accuracy of 20 subjects was 90.03%, significantly higher than the traditional common spatial pattern (CSP) method. The result demonstrates the effectiveness of our data processing model.

本文言語英語
論文番号9390839
ページ(範囲)2214-2219
ページ数6
ジャーナルIEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
DOI
出版ステータス出版済み - 2021
イベント5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021 - Chongqing, 中国
継続期間: 3 12 20213 14 2021

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • 電子工学および電気工学
  • 応用数学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • コンピュータ ビジョンおよびパターン認識

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