Wavelet-based fractal features with active segment selection: Application to single-trial EEG data

Abstract

Feature extraction in brain–computer interface (BCI) work is one of the most important issues that significantly affect the success of brain signal classification. A new electroencephalogram (EEG) analysis system utilizing active segment selection and multiresolution fractal features is designed and tested for single-trial EEG classification. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the proposed system consists of three main procedures including active segment selection, feature extraction, and classification. The active segment selection is based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, and is used to obtain the optimal active time segment in the time–frequency domain. We then utilize a modified fractal dimension to extract multiresolution fractal feature vectors from the discrete wavelet transform (DWT) data for movement classification. By using a simple linear classifier, we find significant improvements in the rate of correct classification over the conventional approaches in all of our single-trial experiments for real finger movement. These results can be extended to see the good adaptability of the proposed method to imaginary movement data acquired from the public databases.