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pro vyhledávání: '"Jesse Sherwood"'
[Finding suitable common feature sets for use in multiclass subject independent brain-computer interface (BCI) classifiers is problematic due to characteristically large inter-subject variation of electroencephalographic signatures. We propose a wrap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b3b4c257e6f11b6ed8c6bfab356906a
https://doi.org/10.1101/2020.02.07.938548
https://doi.org/10.1101/2020.02.07.938548
Autor:
Reza Derakhshani, Jesse Sherwood
Publikováno v:
IJCNN
Given their multiresolution temporal and spectral locality, wavelets are powerful candidates for decomposition, feature extraction, and classification of non-stationary electroencephalographic (EEG) signals for brain-computer interface (BCI) applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1737f3f5b22627b64de54d166d308e4
https://doi.org/10.1109/ijcnn.2009.5178939
https://doi.org/10.1109/ijcnn.2009.5178939
