Decoding of movement-related cortical potentials at different speeds
Author:
Funder
Key Research and Development Program of Zhejiang Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11571-024-10164-3.pdf
Reference39 articles.
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3. Colamarino E, Muceli S, Ibáñez J, Mrachacz-Kersting N, Mattia D, Cincotti F, Farina D (2019) Adaptive learning in the detection of movement related cortical potentials improves usability of associative brain-computer interfaces. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3079–3082 . IEEE
4. Degirmenci M, Yuce YK, Perc M, Isler Y (2023) Statistically significant features improve binary and multiple motor imagery task predictions from EEGS. Front Hum Neurosci 17:1223307
5. Degirmenci M, Yuce YK, Perc M, Isler Y (2024) EEG-based finger movement classification with intrinsic time-scale decomposition. Front Hum Neurosci 18:1362135
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