Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
Qing Yang
Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
Ruimin Wang
Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
Pan Lin
Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
Junfeng Gao
Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China
Yue Leng
Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
Yuankui Yang
Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
Haixian Wang
Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
With the development of the wearable brain-computer interface (BCI), a few-channel BCI system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI system that uses only a few channels of electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) signals to obtain relatively high accuracy. We developed new approaches for signal acquisition and signal processing to improve the performance of this few-channel BCI system. At the signal acquisition stage, source analysis was applied for both EEG and fNIRS signals to select the optimal channels for bimodal signal collection. At the feature extraction stage, phase-space reconstruction was applied to the selected three-channel EEG signals to expand them into multichannel signals, thus allowing the use of the traditional effective common spatial pattern to extract EEG features. For the fNIRS signal, the Hurst exponents for the selected ten channels were calculated and composed of the fNIRS data feature. At the classification stage, EEG and fNIRS features were joined and classified with the support vector machine. The averaged classification accuracy of 12 participants was 81.2% for the bimodal EEG-fNIRS signals, which was significantly higher than that for either single modality.
