A hybrid brain-computer interface using motor imagery and SSVEP Based on convolutional neural network

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradig...

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Autori principali:	Wenwei Luo, Wanguang Yin, Quanying Liu, Youzhi Qu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Taylor & Francis Group 2023-12-01
Serie:	Brain-Apparatus Communication
Soggetti:	brain-computer interface (bci) convolutional neural networks (cnns) electroencephalography (eeg) steady-state visual evoked potential (ssvep) motor imagery (mi)
Accesso online:	http://dx.doi.org/10.1080/27706710.2023.2258938
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