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Motor imagery recognition has been considered an important topic in the brain-computer interface (BCI) community. Due to noises and artifacts in signals, how to gain satisfactory classification accuracy is still a critical issue. We propose in this paper a novel feature to address this issue. The method consists of three steps. Firstly, EEG signals from different electrodes are transformed by Time-Frequency Analysis method, in this paper Hilbert-Huang Transform. A set of features, Degree of Imagery (DOI) are then extracted from the spectrums by the proposed feature extraction method. The features can effectively represent the event-related-desynchronization (ERD) during motor imagery. Experimental results on the BCI 2003 competition dataset III indicate that our method achieves better classification accuracy and higher mutual information (MI) than other researches using the same dataset and with low computational time, which is capable of real-time usage.
Date of Conference: 8-10 June 2011