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.
The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms
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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.