Research on Evaluating EEG Signal Processing Methods for the Development of BCI Systems

Abstract

This article investigates the evaluation of EEG signal processing methods to enhance the development of Brain-Computer Interface (BCI) systems. Given the critical role of EEG signal quality in determining BCI performance, we explore the impact of various noise types—such as artifacts from eye movements, muscle activity, and electronic interference—on signal integrity. We focus on noise filtering techniques, particularly their effectiveness in preserving essential signal components while eliminating unwanted noise. Our research includes a detailed analysis of several common noise processing methods, including Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT), and bandpass filtering. Through comprehensive testing on real EEG signals, we identify optimal strategies for improving signal quality. The findings suggest that while ICA offers high-level noise filtering, it requires expert intervention for effective implementation. In contrast, the bandpass filter demonstrates significant potential for automated noise reduction, providing stable performance with minimal computational cost. Applying these methods before classification has shown to enhance BCI system accuracy significantly. This study contributes to the ongoing efforts to refine BCI algorithms by identifying effective noise-filtering strategies that can be seamlessly integrated into practical applications.