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Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding
Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where the loss could decrease the most, but whether they improve compared to a reference width is untested on EEG. Here, we grow three convolutional backbo
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