Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach
Author:
Affiliation:
1. School of Computer, Qinghai Normal University, Xining, China
2. Departments of Mathematics, Qinghai College of Architectural Technology, Xining, China
3. College of Civil and Hydraulic Engineering, Qinghai University, Xining, Qinghai, China
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/10255842.2024.2346356
Reference25 articles.
1. Al-Nassrawy KK Idrees AK Al-Shammary D. 2022. A novel lossless EEG compression model using fractal combined with fixed-length encoding technique. AI and IoT for Sustainable Development in Emerging Countries: Challenges and Opportunities (pp. 439–454). Cham: Springer International Publishing.
2. Ben Said A Mohamed A Elfouly T. 2017. Deep learning approach for EEG compression in mHealth system. In: 2017 13th International wireless communications and mobile computing conference IWCMC 2017.
3. Bethge D Hallgarten P Grosse-Puppendahl T Kari M Chuang LL Ozdenizci O Schmidt A. 2022. EEG2Vec: Learning affective EEG representations via variational autoencoders. In: Conference proceedings - IEEE international conference on systems man and cybernetics. Vol. 2022-October.
4. Campobello G Quercia A Gugliandolo G Segreto A Tatti E Ghilardi MF Crupi G Quartarone A Donato N. 2021. An efficient near-lossless compression algorithm for multichannel EEG signals. In: 2021 IEEE international symposium on medical measurements and applications MeMeA 2021 - Conference Proceedings.
5. EEG Monitoring After Convulsive Status Epilepticus
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