Motor Imagery EEG Classification with Real-World and EEG-GAN-Generated Data

Accurate classification of EEG signals is crucial for applications such as brain-computer interfaces (BCI). In this study, we examined the effectiveness of long short-term memory (LSTM) networks for classifying EEG signals recorded during six different motor imagery tasks. The dataset included recordings from 16 electrodes and consisted of actions such as forearm supination, elbow flexion, forearm pronation, hand closing, hand opening, and elbow extension. Our model achieved an average F1 score of 0.87 on the real-world EEG data. To evaluate the feasibility of training the model on similar synthetic data, we generated synthetic EEG signals for hand opening and closing tasks using EEG-GAN. When tested on synthetic data, the LSTM model trained solely on real data achieved the average F1 score of 0.76. These results highlight the potential of generative models for augmenting EEG datasets and the robustness of LSTM-based classifiers.