A Novel Generalized EEG Channel Selection Method Using Pearson Correlation Coefficient*

Abstract

Electroencephalography (EEG), as a non-invasive and convenient method for implementing Brain-Computer Interface (BCI), has been widely used in clinical and research fields. EEG data often requires the acquisition of dozens or even hundreds of channels. Channel selection can reduce irrelevant and redundant channels, improve computational efficiency, and enhance the quality of EEG signals. This study introduces a filter method for channel selection based on Pearson correlation coefficient (PCC) with the candidate channel and employs topographic maps of EEG channel scores, derived from data collected across all subjects, to visualize the spatial distribution of channels selected by different methods. In addition, a generalized channel selection algorithm is proposed to determine consistent channels across all subjects in the experimental group. The effectiveness of the proposed method was evaluated on two steady-state visual evoked potential (SSVEP) datasets, and the results indicated that this method exhibits superior performance compared to both the all-channel method and other channel selection methods. And the application of the generalized channel algorithm has further improved the classification performance. This study uses selected generalized channels applied to new subjects with low BCI performance, yielding a significant improvement. The selected channels have a wide range of applicability, helping to simplify EEG acquisition and improve EEG data quality.