Compact model

ShallowFBCSPNet: results on public EEG datasets

ShallowFBCSPNet is Braindecode's name for the shallow ConvNet of Schirrmeister et al. (Human Brain Mapping, 2017). Modeled on filter bank common spatial patterns (FBCSP), it applies a temporal convolution, a spatial convolution across electrodes, squaring, mean pooling and a logarithm, which together approximate log band-power features.

Also known as ShallowFBCSPNet · Shallow ConvNet · ShallowConvNet · 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
ShallowFBCSPNet	Seated motor imagery · cue-gated replay	Command detection ≤3 s	43.3%	4
ShallowFBCSPNet	Seated motor imagery · cue-gated replay	Idle false activation	1.7%	4

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