Motor imagery EEG discrimination using Hilbert-Huang Entropy

The selection of the optimal feature of EEG signals is important for the discrimination of mental tasks in brain-computer interface (BCI) research. This research presents a new technique for feature extraction of EEG signals sampled from subject executing left and right hand motor imagery (MI) using Hilbert- Huang Entropy (HHE). In our method, the raw signal is analysed with an elliptical band-pass filter and Hilbert-Huang Transform (HHT). The marginal spectra of beta and mu bands are the interesting features calculated from the Hilbert-Huang spectrum of the selected Intrinsic Mode Functions (IMF) of the filtered EEG signal. The Shannon entropy (SE) is then utilized within the framework of the HHT algorithm. The formed feature vector calculated by the SE transform is utilized to train a support vector machine (SVM) classifier for classification. The performance of the new method is compared to the HHT algorithm, which indicates the HHE algorithm is promising for BCI classification