Automatic Frequency Bands Segmentation Using Statistical Similarity for Power Spectrum Density Based Brain Computer Interfaces

Abstract

Power spectrum density (PSD) of electro-encephalogram (EEG) signals is a widely used feature for Brain Computer Interfaces (BCI). Usually, PSD features are integrated over different frequency bands, such as delta, theta, alpha, beta, gamma, which are based on well-established interpretations of EEG signals in prior experimental and clinical contexts. However, these predefined frequency bands do not necessarily relate to the optimal features for various BCI applications. In this paper, we propose an alternative feature dimensionality reduction method, which automatically determines the optimal number and the range of frequency bands. We applied the proposed method on EEG classification in the context of Augmented Cognition (AugCog) using BCI. The experimental results show that the proposed method can extract more robust features than features manually extracted from predefined frequency bands.