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.
Dropout: a simple way to prevent neural networks from overfitting.The Journal of Machine Learning Research, 15(1):1929–1958, 2014
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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.