Affiliations: College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, China | College of Life Science & Biological Engineering, Beijing University of Technology, Beijing, China
Note: [] Corresponding author. Mingai Li, College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China. Tel.: +86 10 6739 6309; Fax: +86 10 6739 1625; E-mail: limingai@bjut.edu.cn The work was supported by the Science and Technology Project of Beijing Municipal Education Commission (No. KM201110005005), the Natural Science Foundation of Beijing (Nos. 7132021, 4112011), National Natural Science Foundation (No. 61201362) and the Fundamental Research Foundation of Beijing University of Technology (No. X4002011201101).
Abstract: The adaptivity of feature extraction is a key problem in rehabilitation with brain computer interface. A multi-domain feature fusion method was proposed for EEG. The method is mainly based on Hilbert-Huang transform (HHT) and common spatial subspace decomposition (CSSD) algorithm and denoted as HCSSD. Firstly, a relative distance criterion is defined to select the optimal combination of channels in consideration of the distinction of event-related desynchronization (ERD) extent induced by different motor imagery tasks. Then HHT and CSSD are applied to extract the time-frequency feature and spatial feature for optimal EEG signals respectively. Furthermore, serial feature fusion strategy is employed to construct time-frequency-spatial feature. Finally, learning vector quantization (LVQ) neural network is designed to classify the motor imagery electrocorticography (ECoG) data in BCI Competition III. The data were recorded from the same subject and with the same mental tasks, but on two days with about one week in between. The average recognition accuracy is 92% with much less channels used. Experiment results show that HCSSD can enhance the adaptability and robustness of feature extraction, and the recognition accuracy is also improved. This is helpful for further research of portable BCI system in rehabilitation field.
