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Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding.

, , and . BCI, page 1-6. IEEE, (2018)

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Machine-learning-based diagnostics of EEG pathology., , , , , , , and . NeuroImage, (2020)Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features., , , and . NeurIPS, (2020)A Large-Scale Evaluation Framework for EEG Deep Learning Architectures., , , , , and . SMC, page 1039-1045. IEEE, (2018)Wireless power distribution system for brain implants., , , , , , , and . I2MTC, page 1249-1254. IEEE, (2015)Deep Invertible Networks for EEG-based brain-signal decoding., and . CoRR, (2019)A reusable benchmark of brain-age prediction from M/EEG resting-state signals., , , , , , , and . NeuroImage, (2022)When less is more: Simplifying inputs aids neural network understanding., , , and . CoRR, (2022)Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning., , , and . CoRR, (2023)Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills., , , , , , , , , and 1 other author(s). ECMR, page 1-6. IEEE, (2017)Cross-Paradigm Pretraining of Convolutional Networks Improves Intracranial EEG Decoding., , , , , , , and . SMC, page 1046-1053. IEEE, (2018)