Toward the TCN-based Real-Time BCI System for Target Detection

This study focuses on developing a real-time Brain-Computer Interface (BCI) system, specifically designed for military applications, to enhance target detection in rapid serial visual presentation (RSVP) tasks. The proposed BCI system utilizes electroencephalogram (EEG) signals based on dry electrodes, known for their exceptional temporal resolution, to identify swiftly specific target symbols within sequences of visual stimuli. Leveraging deep learning techniques, particularly Temporal Convolutional Networks (TCN), this study demonstrates the accuracy and efficiency improvement in target detection for RSVP tasks. According to our findings, the adaptability and efficacy of TCN in handling temporal dynamics of EEG signals exhibit outstanding performance in target detection, thus offering the potential for accurate and efficient real-time BCI system.