Advanced Deep Learning Method Selection for BCI: A Comparison Study Across Different Applications

Brain-Computer Interface (BCI) is a rapidly advancing field, offering novel pathways for communication and control. Deep learning (DL) has shown significant promise in decoding brain signals for BCI applications due to its ability to learn complex patterns from electroencephalography (EEG) data. However, the optimal selection of DL modules—including data preprocessing techniques, feature engineering strategies, position embedding methods, backbone architectures, and dimension reduction methods—often varies with the specific BCI application and dataset characteristics. This paper presents a systematic comparative study investigating the impact of different DL module choices on model performance across diverse BCI applications. We evaluate a baseline DL architecture and its variants on two distinct datasets: the BCI Competition IV-2a dataset for motor imagery classification and the MODMA resting-state dataset for depression detection. Ultimately, this study aims to provide practical guidance for selecting appropriate DL components for specific BCI tasks, revealing how different module choices influence model performance and enhance the model’s ability to interpret EEG data.