EEG feature selection using mutual information and support vector machine: A comparative analysis.

The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features by feature selection methods based on forward-backward procedure and mutual information as relevance criteria. Results confirm that fractional Fourier transform coefficients present very good performance and also the possibility of using some combination of this features to improve the performance of the classifier. To reinforce the relevance of the study, we carry out 1000 independent runs using a bootstrap approach, and evaluate the statistical significance of the F(score) results using the Kruskal-Wallis test.

New approach in features extraction for EEG signal detection.

This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.