# EEGNet: results on public EEG datasets

Compact model

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. ↗](https://doi.org/10.1088/1741-2552/aace8c)

Directory status: Evaluated · [Official code ↗](https://github.com/vlawhern/arl-eegmodels) · [Implementation used here ↗](https://github.com/braindecode/braindecode/tree/v1.5.1) · Description sources [arxiv.org](https://arxiv.org/abs/1611.08024) · [doi.org](https://doi.org/10.1088/1741-2552/aace8c) · [pubmed.ncbi.nlm.nih.gov](https://pubmed.ncbi.nlm.nih.gov/29932424/) · [github.com](https://github.com/braindecode/braindecode/blob/v1.5.1/braindecode/models/eegnet.py)

## Published results

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

### [ds003810](https://bci.report/datasets/ds003810/) · Core matrix · motor imagery & rest

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 50.0% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Rest versus right-hand imagery | Balanced accuracy | 71.0% (65.0%–76.3%) | 10 |

### [EEGMAT](https://bci.report/datasets/eegmat/) · Core matrix · arithmetic & rest

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 50.0% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Serial subtraction versus resting EEG | Balanced accuracy | 67.6% (62.8%–72.5%) | 36 |

### [BETA](https://bci.report/datasets/beta/) · Core matrix · SSVEP, 8 channels

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 2.5% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 55.8% (50.1%–61.3%) | 70 |

### [BETA](https://bci.report/datasets/beta/) · Core matrix · SSVEP, 4 channels

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 2.5% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 44.1% (38.6%–49.6%) | 70 |

### [ds006593](https://bci.report/datasets/ds006593/) · Core matrix · P300 target ERP

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 50.0% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Visual target versus nontarget events | Balanced accuracy | 53.3% (51.7%–54.9%) | 21 |

### [TMNRED / ds005383](https://bci.report/datasets/tmnred/) · Core matrix · semantic target ERP

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 50.0% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Reading · target versus nontarget events | Balanced accuracy | 61.4% (58.9%–64.0%) | 30 |

### [EESM19 scalp subset](https://bci.report/datasets/eesm19/) · Core matrix · sleep staging

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · Chance level 20.0% · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Five stages · balanced scalp-EEG sample | Balanced accuracy | 56.5% (54.9%–58.3%) | 20 |

### [ds005342](https://bci.report/datasets/ds005342/) · Core matrix · idle & command

Read with its protocol: [Core matrix (home page)](https://bci.report/#overview) · [experiments.json](https://bci.report/data/experiments.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Seated motor imagery · cue-gated replay | Command detection ≤3 s | 1.7% | 4 |
| [EEGNet](https://bci.report/methods/eegnet/) | Seated motor imagery · cue-gated replay | Idle false activation | 0.0% | 4 |

### [Wearable SSVEP BCI dataset (dry and wet electrodes)](https://bci.report/datasets/wearable-ssvep-102/) · Dry vs. wet sensor transfer

Read with its protocol: [Dry vs. wet electrodes](https://bci.report/topics/dry-vs-wet/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

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

- 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%)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [EEGNet](https://bci.report/methods/eegnet/) | Trained on wet, tested on wet | Balanced accuracy | 75.0% (70.3%–79.5%) | 102 |
| [EEGNet](https://bci.report/methods/eegnet/) | Trained on wet, tested on dry | Balanced accuracy | 45.6% (40.5%–50.8%) | 102 |
| [EEGNet](https://bci.report/methods/eegnet/) | Trained on dry, tested on wet | Balanced accuracy | 64.0% (59.1%–68.8%) | 102 |
| [EEGNet](https://bci.report/methods/eegnet/) | Trained on dry, tested on dry | Balanced accuracy | 56.8% (51.9%–61.6%) | 102 |

[All methods →](https://bci.report/methods/) · [All datasets →](https://bci.report/datasets/)

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Markdown copy of https://bci.report/methods/eegnet/, generated from the published page. Figures are aggregate results; terms of use: https://bci.report/data-use/