ConvNets with adaptive moments, batch normalization and dropout on raw EEG outperform conventional fully-connected networks using spectral features for subject-independent motor imagery classification.
Analyzing neural time series data: theory and practice
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Deep Learning with ConvNET Predicts Imagery Tasks Through EEG
ConvNets with adaptive moments, batch normalization and dropout on raw EEG outperform conventional fully-connected networks using spectral features for subject-independent motor imagery classification.