A Transformer-Enhanced Method Integrating Deep Learning and Spatial Temporal Data for Raw EEG Classification

Deciphering raw EEG signals can be tricky since they often have a lot of noise and complex patterns. This is especially important for brain-computer interfaces (BCIs), where it's crucial to get the signals accurate. To address this issue, we developed a new deep learning model that combines convolutional neural networks (CNNs), Transformer encoders, and LSTM units. This combination helps the model capture both detailed local features and larger trends in the EEG data. We also set up a solid preprocessing system that includes bandpass filtering, Independent Component Analysis (ICA) to get rid of noise, and normalization with the MNE library to keep the data consistent. We tested our model on some public motor imagery EEG datasets and got a classification accuracy of 98.52%. The area under the curve (AUC) was 0.9994, and the average precision was 0.9995. These results indicate that our methodology is highly effective in extracting meaningful information from unprocessed EEG signals, thereby enhancing the performance of brain-computer interfaces (BCIs).