Signal Processing of Motor Imagery EEG Waves Using Empirical Mode Decomposition

Abstract

Electroencephalogram (EEG) is the most convenient method for recording the electrical activities of the brain, for Brain Computer Interface (BCI) applications. This EEG data is notoriously noisy. A variety of frequency estimation techniques are used in feature extraction . This is possible due to the presence of information of interest in frequency bands which are well defined. The application of EMD (Empirical Mode Decomposition) on the recorded EEG waves of subjects’, renders time-frequency data depicting instantaneous frequencies. EMD is chosen to obtain Hilbert–Huang Transform (HHT) of the data which is chosen over Fourier Transform (FT) owing to the nonstationarity, closely spaced frequency bands of interest and low SNR of the recorded data. HHT of the data can be used to obtain a feature or signature, which can be used as a command signal for various BCI applications.