Research on EEG signal discrimination methods based on ConvTranse

The brain is the center of high-level neural activity in the human body, with billions of neurons that connect and process human information through mutual connections. Therefore, studying brain-computer signals has profound significance. We investigates a method for target classification in brain-computer interface systems, proposing a brainwave signal identification method based on ConvTrans. This method aims to improve classification accuracy. It identifies targets in the test set with as few test rounds as possible, mainly including two parts: P300 detection and character recognition. P300 detection is used to determine whether the P300 wave is detected, and the key task of the character recognition part is to identify the specific character that the subject is looking at, combining the results of the previous classification task. Through multiple repeated experiments, it can reduce the probability of identifying the P300 with the greatest interference in rows and columns, thereby determining the target character. This method has stable locking characteristics and high temporal precision, allowing users to achieve high recognition accuracy without complex training.