The successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation and enable more effective utilization of data for improving the performance on target domain, we propose a Domain Adaptation (DA) framework based on LSTM-autoencoder. Our experiments encompassed the following: (1) A comparison between LSTM-autoencoder, GRU-autoencoder, and the commonly used convolutional autoencoder (CAE) in EEG compression. (2) A comparison between our proposed DA method and the MMD-based DA method, as well as Fine-tuning transfer learning. The results demonstrate the following: (1) LSTM-autoencoder outperforms other models in both subject-specific and cross-subject scenarios. (2) Using transfer learning improves the performance of LSTM-autoencoder on the target subject. (3) Our proposed method outperforms maximum mean discrepancy (MMD)-based domain adaptation and fine-tuning approaches, resulting in a more significant enhancement.
Keywords: EEG compression; LSTM-autoencoder; domain adaptation.