Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach

Yongfei Liu, Fan Yang and Binbin Wu

Computer Methods in Biomechanics and Biomedical Engineering, 2025, vol. 28, issue 12, 1857-1870

Abstract: 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10255842.2024.2346356 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gcmbxx:v:28:y:2025:i:12:p:1857-1870

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gcmb20

DOI: 10.1080/10255842.2024.2346356

Access Statistics for this article

Computer Methods in Biomechanics and Biomedical Engineering is currently edited by Director of Biomaterials John Middleton

More articles in Computer Methods in Biomechanics and Biomedical Engineering from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().