Feature extraction of SSVEP-based brain-computer interface with ICA and HHT method

For the problem of extracting feature of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system efficiently, a method based on independent component analysis (ICA) and Hilbert-Huang transform (HHT) is proposed in this paper. Firstly, Band-pass filter is applied to preprocess the electroencephalograph (EEG) of SSVEP. Secondly, the independent components are acquired from filtered signals with ICA. Thirdly, HHT is applied to decompose the independent components to obtain the intrinsic mode function (IMF) needed. Finally, frequency domain analysis is applied to analyse IMF. The experiments show that the proposed method is feasible in feature extraction and the noise can be removed.