Research on Signal Preprocessing Methods based on Brain-computer Interface

Abstract

With the rapid advancement of neuroscience and engineering technology, Brain-Computer Interface (BCI) technology has garnered widespread attention and research interest as an innovative means of directly connecting the human brain to external devices. BCI has emerged as a promising technology for facilitating direct communication between the brain and external devices. However, the quality of EEG signals recorded by BCI systems is often affected by various artifacts, such as eye movements, muscle activities, and powerline interference. Therefore, signal preprocessing plays a crucial role in improving the performance of BCI systems. In this paper, brain-computer interface (BCI) signal preprocessing methods are reviewed. The principles, advantages, and disadvantages of these methods are summarized, and the future development direction of BCI signal preprocessing is discussed. Overall, with these preprocessing methods, the BCI system is able to recognize and interpret EEG signals more accurately and reliably, providing better support for brain-computer interaction. The potential applications of deep learning in EEG signal preprocessing, especially in the recognition and removal of complex artifacts, are of great significance.