Motor imagery EEG signal classification scheme based on wavelet domain statistical features

Classification of electroencephalogram (EEG) data for different motor imagery (MI) tasks is a major concern in the brain-computer interface (BCI) applications. In this paper, an efficient feature extraction scheme is proposed based on the discrete wavelet transform (DWT) of the EEG signal. The EEG data of each channel is windowed into several frames and DWT is performed on each frame of data. Considering only the approximate DWT coefficients, a set of statistical features are extracted, namely wavelet domain energy, entropy, variance, and maximum. In order to reduce the dimension of the proposed feature vector, which is composed of average statistical feature values of all channels, principal component analysis (PCA) is employed. For the purpose of classification, k nearest neighbor (KNN) classifier is employed. Proposed classification scheme not only offers significant reduction in feature dimensionality but also provides satisfactory classification accuracy. For the purpose of performance analysis, publicly available MI dataset IVa of BCI Competition-III is used and a very satisfactory performance is obtained in classifying the MI data in two classes, namely right hand and right foot MI tasks.