Stochastic relevance analysis of epileptic EEG signals for channel selection and classification

Abstract

Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.

Energy extraction method for EEG channel selection

“…The combination of spatial covariance is used as a factorized formula in (6) to whiten the transformation process in (7):…”

“…There are many reasons why the channel selection method is important in optimizing BCI. Firstly, one drawback of EEG signals is that over fit to noise increases with the number of task-irrelevant features [6,7]. Secondly, it is difficult to understand which part of the brain generates class-relevant activity [8,9].…”