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

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Article number9390839
Pages (from-to)2214-2219
Number of pages6
JournalIEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
DOIs
Publication statusPublished - 2021
Event5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021 - Chongqing, China
Duration: Mar 12 2021Mar 14 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Computer Vision and Pattern Recognition

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