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An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0. 31% with 7. 6x memory footprint reduction and a small accuracy loss of 2. 51% with 15x reduction.
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