EEGminer: discovering interpretable features of brain activity with learnable filters
Siegfried Ludwig,
Stylianos Bakas,
Dimitrios A Adamos
et al.
Abstract:Objective: The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity. Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Speci… Show more
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