The adaptivity and real-time performance of feature extraction method are crucial in brain-computer interface.Based on Hilbert-Huang transform (HHT) and common spatial subspace decomposition (CSSD) algorithm

a novel feature extraction method

denoted as HCSSD

was proposed.Firstly

the motor imagery electroencephalography (EEG)/ electrocorticography (ECoG) was preprocessed

and a relative distance criterion was defined to select the optimal combination of channels.Secondly

Hilbert instantaneous energy spectrum and marginal energy spectrum of EEG/ECoG were calculated to extract time feature and frequency feature respectively.Then CSSD was applied to extract spatial feature.Furthermore

serial feature fusion strategy was adopted to obtain time-frequency-spatial feature.Finally

learning vector quantization neural network was designed to classify the EEG/ECoG data.The average recognition accuracy was 92% for the left small finger and tongue motor imagery ECoG tasks.Experiment results show that HCSSD can enhance the adaptivity and real-time performance of feature extraction

with the recognition accuracy improved.This method provides a new idea for the application of portable BCI system in rehabilitation field.