Benchmarking brain–computer interface algorithms: Riemannian approaches vs convolutional neural networks

To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural...

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Vydané v:	Journal of neural engineering Ročník 21; číslo 4
Hlavní autori:	Eder, Manuel, Xu, Jiachen, Grosse-Wentrup, Moritz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England IOP Publishing 01.08.2024
Predmet:	Algorithms benchmarking Benchmarking - methods brain-computer interface Brain-Computer Interfaces classification convolutional neural network electroencephalography Electroencephalography - methods Evoked Potentials, Visual - physiology Humans Imagination - physiology machine learning Neural Networks, Computer Riemannian geometry Riemannian geometry electroencephalography benchmarking brain-computer interface convolutional neural network classification machine learning
ISSN:	1741-2560, 1741-2552, 1741-2552
On-line prístup:	Získať plný text
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