The Channel Selection in the Analysis of Binocular Disparity EEG Data Processing

Binocular disparity is a crucial cue for perceiving depth, and investigating its neural mechanisms using EEG can deepen our understanding of stereo vision. However, analyzing EEG data is often complex and time-consuming, which may hinder researchers’ efficiency. To address this issue, we designed a channel selection method based on average power spectral density (PSD) and Iterative Deepening Search (IDS) to streamline data analysis while maintaining accuracy. We collected EEG signals from four subjects while they viewed random dot stereograms (RDS) with disparities of 0′′ and 1000′′. Our results demonstrate that by reducing the number of channels by half, the proposed channel selection method improved the classification accuracy of the two disparities by 20% compared to using all channels. This approach provides a promising tool for efficient and accurate EEG data analysis in the investigation of binocular disparity and stereo vision.