EEG based dynamic RDS recognition with frequency domain selection and bispectrum feature optimization

Abstract

Background: Stereopsis plays a vital role in many aspects of human daily life. Random-dot stereogram (RDS) is often used to detect stereoacuity and perform research on visual cognition. Electroencephalogram (EEG) is one of the commonly adopted visual cognition techniques due to its noninvasive collection.

New method: In this study, a methodology named WPT-BED based on wavelet packet transform (WPT) and bispectral eigenvalues of differential signals (BED) is proposed, which can classify the three-pattern EEG signals evoked by dynamic RDS (DRDS). Specifically, the signals are decomposed into different frequency bands by WPT. The appropriate sub-bands are selected for reconstruction. Finally, the optimized bispectrum features are extracted for classification to achieve higher accuracy.

Results: The classification performance of the proposed method in different periods of signal processing are investigated. The method WPT-BED has the highest classification accuracy 84.38%, and the average classification accuracy is 73.98%. The active channels with higher accuracy are focused on the visual pathway in the human cerebral cortex.

Comparison with existing methods: Comparison with other methods for EEG signals classification is performed to identify the effectiveness of the proposed methodology.

Conclusions: The proposed methodology can effectively distinguish the EEG signals evoked by DRDS. It demonstrates the feasibility of DRDS recognition based on EEG.

Keywords: Bispectrum; Dynamic random-dot stereogram (DRDS); Electroencephalogram (EEG); Stereoacuity; Wavelet packet transform (WPT).