Compact model

EEGNet: results on public EEG datasets

EEGNet is a compact convolutional neural network for EEG-based brain–computer interfaces, proposed by Lawhern et al. (U.S. Army Research Laboratory; Journal of Neural Engineering, 2018) and tested on P300, ERN, MRCP and SMR data. It applies a temporal convolution, a depthwise spatial convolution and a separable convolution, followed by a softmax classifier.

Also known as EEGNet · EEGNet-8,2 · EEGNet-4,2 · EEGNetv4 · Lawhern et al. 2018

Reference Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering 15(5):056013, 2018. ↗

Directory status: Evaluated · Official code ↗ · Implementation used here ↗ · Description sources arxiv.org · doi.org · pubmed.ncbi.nlm.nih.gov · 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.

ds003810 · Core matrix · motor imagery & rest

Read with its protocol: Core matrix (home page) · Chance level 50.0% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	Rest versus right-hand imagery	Balanced accuracy	71.0%65.0%–76.3%	10

EEGMAT · Core matrix · arithmetic & rest

Read with its protocol: Core matrix (home page) · Chance level 50.0% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	Serial subtraction versus resting EEG	Balanced accuracy	67.6%62.8%–72.5%	36

BETA · Core matrix · SSVEP, 8 channels

Read with its protocol: Core matrix (home page) · Chance level 2.5% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	40 visual targets · 8 posterior electrodes	Balanced accuracy	55.8%50.1%–61.3%	70

BETA · Core matrix · SSVEP, 4 channels

Read with its protocol: Core matrix (home page) · Chance level 2.5% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	40 visual targets · 4 posterior electrodes	Balanced accuracy	44.1%38.6%–49.6%	70

ds006593 · Core matrix · P300 target ERP

Read with its protocol: Core matrix (home page) · Chance level 50.0% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	Visual target versus nontarget events	Balanced accuracy	53.3%51.7%–54.9%	21

TMNRED / ds005383 · Core matrix · semantic target ERP

Read with its protocol: Core matrix (home page) · Chance level 50.0% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	Reading · target versus nontarget events	Balanced accuracy	61.4%58.9%–64.0%	30

EESM19 scalp subset · Core matrix · sleep staging

Read with its protocol: Core matrix (home page) · Chance level 20.0% · experiments.json

Method	Condition	Metric	Value	People
EEGNet	Five stages · balanced scalp-EEG sample	Balanced accuracy	56.5%54.9%–58.3%	20

ds005342 · Core matrix · idle & command

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

Method	Condition	Metric	Value	People
EEGNet	Seated motor imagery · cue-gated replay	Command detection ≤3 s	1.7%	4
EEGNet	Seated motor imagery · cue-gated replay	Idle false activation	0.0%	4

Wearable SSVEP BCI dataset (dry and wet electrodes) · Dry vs. wet sensor transfer

Read with its protocol: Dry vs. wet electrodes · deployment-topics.json

EEGNet · Trained on wet, tested on wet

75.0% (70.3%–79.5%)

EEGNet · Trained on wet, tested on dry

45.6% (40.5%–50.8%)

EEGNet · Trained on dry, tested on wet

64.0% (59.1%–68.8%)

EEGNet · Trained on dry, tested on dry

56.8% (51.9%–61.6%)

Balanced accuracy, 4 configurations. Dot: the estimate; line: 95% interval; dashed line: chance level where the payload records one.

Method	Condition	Metric	Value	People
EEGNet	Trained on wet, tested on wet	Balanced accuracy	75.0%70.3%–79.5%	102
EEGNet	Trained on wet, tested on dry	Balanced accuracy	45.6%40.5%–50.8%	102
EEGNet	Trained on dry, tested on wet	Balanced accuracy	64.0%59.1%–68.8%	102
EEGNet	Trained on dry, tested on dry	Balanced accuracy	56.8%51.9%–61.6%	102

All methods → · All datasets →