Combination of wavelet packet transform and Hilbert-Huang transform for recognition of continuous EEG in BCIs

Abstract

An improved Hilbert-Huang transform(HHT) combined with wavelet packet transform(WPT) is proposed for recognizing continuous electroencephalogram(EEG) in brain computer interfaces(BCIs). The HHT consists of empirical mode decomposition(EMD) and Hilbert-Huang spectrum(HHS). Firstly, the WPT decomposes the signal into a set of narrow band signals, then a series of Intrinsic Mode Functions(IMFs) can be obtained after application of the EMD. Whereafter, two kinds of screening processes are conducted on the first two IMFs of each narrow band signal to remove unrelated IMFs. Hilbert Transform(HT) is then employed to calculate the HHS, from which energy changes in mu-rhythm and beta-rhythm can be recognized clearly. Datasets I of BCI competition IV 2008 are analyzed. The results show that the proposed method has better discriminability than the traditional HHT among different states. The proposed algorithm has the potentiality to trace mu-rhythm and beta-rhythm changes, which paves a way for a more enhanced BCI performance.