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We propose a self-supervised learning (SSL) approach for cognitive workload classification using wavelet-based augmentations of EEG signals.

Our patient-independent model achieved an overall accuracy of 78% in detecting FoG events using both medication ‘On’ and ‘Off’ state data.

We present a data-driven approach for stress detection based on convolutional neural networks while addressing the problems of the best sensor channel and the lack of knowledge about stress episodes.