Channel-weighted brain topography of the FDCN-C model in the BCI-C IV 2a dataset.

Channel-weighted brain topography of the FDCN-C model in the BCI-C IV 2a dataset.

<p>Channel-weighted brain topography of the FDCN-C model in the BCI-C IV 2a dataset.</p>

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Bibliographic Details
Main Author:	Hong-Jie Liang (11495982) (author)
Other Authors:	Ling-Long Li (20308164) (author), Guang-Zhong Cao (6444716) (author)
Published:	2024
Subjects:	Physiology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizing offset parameters two public datasets mi )- electroencephalography diverse receptive fields deformable convolutional network continuous time scales computed multiple times utilizes convolution kernels dimensional convolution layer deformable convolution network convolution kernel size temporal feature extraction model &# 8217 classification accuracy obtained extracting frequency information crop classification module original eeg data frequency enhancement module dilated convolution frequency enhancement crop module temporal domain eeg data channel information baseline model utilization efficiency spatial domain important role enables motor eeg decoding disabled patients deep learning decoding plays computer interface calculating attention bci ), art methods ablation study
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Summary:	<p>Channel-weighted brain topography of the FDCN-C model in the BCI-C IV 2a dataset.</p>

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