Compact model

Deep4Net: results on public EEG datasets

Deep4Net is Braindecode's name for the deep ConvNet of Schirrmeister et al. (Human Brain Mapping, 2017). It stacks four convolution–max-pooling blocks with ELU activations; the first block is split into a convolution over time and a spatial filter across all electrodes, and a dense softmax layer gives the class output.

Also known as Deep4Net · Deep ConvNet · DeepConvNet · Schirrmeister et al. 2017

Reference Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38(11):5391–5420, 2017. ↗

Directory status: Evaluated · Official code ↗ · Description sources arxiv.org · pmc.ncbi.nlm.nih.gov · doi.org · github.com · github.com · github.com

Published results

Grouped by dataset. Compare figures within a group only: across groups the task, cohort, chance level and electrode layout all change.

ds005342 · Core matrix · idle & command

Read with its protocol: Core matrix (home page) · experiments.json

Method	Condition	Metric	Value	People
Deep4Net	Seated motor imagery · cue-gated replay	Command detection ≤3 s	3.3%	4
Deep4Net	Seated motor imagery · cue-gated replay	Idle false activation	1.7%	4

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