Time-frequency series based movement imagery classification

Abstract

This paper proposes a novel pre-processing and feature extraction method for movement imagery classification based on Electroencephalogram (EEG) signal for Brain-Computer Interface (BCI). The proposed pre-processing method considers the time-frequency analysis of EEG signal. The Short Time Fourier Transforms (STFT) based time-frequency analysis is applied to generate the Time-Frequency Series (TFS) of Electrooculogram (EOG) corrected EEG signal. Further, the generated TFS of particular frequency bin is transformed into time domain signal by Inverse Short Time Fourier Transforms (ISTFT). The generated time domain signal of particular frequency bin is passed through feature extraction method. An asymmetry coefficient based on Hjorth parameter as a feature extracted for each frequency bin and further subjected to Support Vector Machine (SVM) classifier for their classification. The proposed method of EEG signal pre-processing and feature extraction outperforms the conventional method of EEG signal pre-processing like Discriminative Frequency Band Common Spatial Pattern (DFBCSP).