The goal of this report is to optimize the classification of electroencephalography (EEG) data, which is provided by the Brain-Computer Interaction (BCI) Competition[2]. To achieve this, deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformer (ViT) Model are employed and compared in terms of their performance. The report describes the methodology used in this project, including data preprocessing, model architecture, and evaluation metrics. By analyzing the results, this study provides insights into the effectiveness of deep learning models in improving the accuracy of EEG data classification, which is relevant to future research in the field of BCI.
https://github.com/YingluDeng/eeg_classification_research/blob/main/egg_classification_report.pdf