BCI ReportCompact 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.
Measured on: ds003810; EEGMAT; BETA; ds006593; TMNRED / ds005383; EESM19 scalp subset; ds005342 · Protocols: Motor imagery & rest; Arithmetic & rest; SSVEP · 8 channels; SSVEP · 4 channels; P300 target ERP; Semantic target ERP; Sleep staging; Idle & command
Also known as Deep4Net · Deep ConvNet · DeepConvNet · Schirrmeister et al. 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
Compare figures within a group only: task, cohort, chance level and electrode layout can differ between groups. One row is above another only where their 95% intervals do not overlap.
ds003810 · Core matrix · motor imagery & rest
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | Rest versus right-hand imagery | Balanced accuracy | 68.6%63.3%–74.0%One fixed seed and training budget, as the EEGNet cell | 10 |
EEGMAT · Core matrix · arithmetic & rest
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | Serial subtraction versus resting EEG | Balanced accuracy | 61.3%57.5%–65.2%One fixed seed and training budget, as the EEGNet cell | 36 |
BETA · Core matrix · SSVEP, 8 channels
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 52.8%47.3%–58.2%One fixed seed and training budget, as the EEGNet cell | 70 |
BETA · Core matrix · SSVEP, 4 channels
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 38.7%33.7%–43.8%One fixed seed and training budget, as the EEGNet cell | 70 |
ds006593 · Core matrix · P300 target ERP
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | Visual target versus nontarget events | Balanced accuracy | 50.8%50.0%–51.6%Braindecode's automatically scaled Deep4Net for 1-s input (no temporal pooling, 229-sample classifier, 235,752 parameters), not the original Deep4Net configuration. The interval's lower end, 50.04 %, prints as 50.0; it sits 0.04 pp above the 50 % reference. | 21 |
TMNRED / ds005383 · Core matrix · semantic target ERP
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | Reading · target versus nontarget events | Balanced accuracy | 57.4%55.4%–59.5%Braindecode's automatically scaled Deep4Net for 1-s input (no temporal pooling, 229-sample classifier, 242,627 parameters), not the original Deep4Net configuration. | 30 |
EESM19 scalp subset · Core matrix · sleep staging
| Method | Condition | Metric | Value | People |
|---|---|---|---|---|
| Deep4Net | Five stages · balanced scalp-EEG sample | Balanced accuracy | 45.2%42.7%–47.7%One fixed seed and training budget, as the EEGNet cell | 20 |
ds005342 · Core matrix · idle & command
Cite this page
BCI Report (2026). Deep4Net: results on public EEG datasets. https://bci.report/methods/deep4net/
Figures from releases core-matrix-update-20261106 (2026-11-06), research-preview-20260920 (2026-09-20). Cite the upstream datasets as well: each dataset’s page gives its credit.
Every release is archived on Zenodo: doi:10.5281/zenodo.23123296. BibTeX for the site and its releases → · CITATION.cff ↗