Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN-Reference-Cited by-同舟云学术

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Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN

Published:2024-04-10 Issue: Volume: Page:
ISSN:1871-4080
Container-title:Cognitive Neurodynamics
language:en
Short-container-title:Cogn Neurodyn

Author:

Ma Jun,Yang Banghua,Rong Fenqi,Gao Shouwei,Wang Wen

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

National Major Science and Technology Projects of China

Shanghai Industrial Collaborative Technology Innovation Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11571-024-10100-5.pdf

Reference38 articles.

1. Ai Q, Zhao M, Chen K, Zhao X, Ma L, Liu Q (2022) Flexible coding scheme for robotic arm control driven by motor imagery decoding. J Neural Eng 19(5):056008. https://doi.org/10.1088/1741-2552/ac84a9

2. Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Shamim Hossain M (2019) Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener Comput Syst 101:542–554. https://doi.org/10.1016/j.future.2019.06.027

3. Banluesombatkul N, Ouppaphan P, Leelaarporn P, Lakhan P, Chaitusaney B, Jaimchariyatam N, Wilaiprasitporn T (2020) MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. IEEE J Biomed Health Inform 25(6):1949–1963. https://doi.org/10.1109/JBHI.2020.3037693

4. BBCI Competition Datasets, http://www.bbci.de/competition. 2018

5. Cai Y, She Q, Ji J, Ma Y, Zhang J, Zhang Y (2022) Motor imagery EEG decoding using manifold embedded transfer learning. J Neurosci Methods 370:109489. https://doi.org/10.1016/j.jneumeth.2022.109489

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