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.