High Resolution Time-Frequency Analysis for Event-Related Electroencephalogram

A high resolution time-frequency analysis method to discriminate among mental tasks for the brain-computer interface (BCI) was presented. It estimated the time-frequency energy of electroencephalogram (EEG) with matching pursuit and Wigner-Ville distribution. Matching pursuit decomposed event-related EEG into a linear expansion of waveforms. A time-frequency energy distribution was derived by adding the Wigner-Ville distribution of each selected atoms. With the prior knowledge, the approach obtained the ratios of energy value over the motor-cortex as extracted features. The classifying time segment was determined according to Mahalanobis distance, and the feature vectors in this segment were used to train an artificial neural network (ANN) as a classifier. The results showed that the minimum error rates for 4 subjects were all lower than 15%, and the maximum mutual information were more than 0.6. The proposed method gives generally applicable, robust and high-resolution estimates