# ds003810: EEG decoding results

Motor imagery / rest

OpenNeuro ds003810 is an EEG dataset of kinesthetic motor imagery of dominant-hand grasping versus rest, recorded from 10 participants without previous BCI experience using a low-cost OpenBCI Cyton + Daisy board with 15 electrodes over sensorimotor areas. Released on OpenNeuro by Peterson et al. and described in Data in Brief (2022).

**Also known as** ds003810 · Motor Imagery vs Rest - Low-Cost EEG System · A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware · Peterson et al. (2022)

Description sources [openneuro.org](https://openneuro.org/datasets/ds003810) · [pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/) · [doi.org](https://doi.org/10.1016/j.dib.2022.108225) · [doi.org](https://doi.org/10.1016/j.heliyon.2020.e03425)

## Where it appears

- [Core matrix (home page)](https://bci.report/#overview)
- [Does pretraining help EEG foundation models like LaBraM and CBraMod?](https://bci.report/topics/does-pretraining-help/)

## Published results

Every figure below is copied from a reviewed download, not recomputed for this page. Read each group with its protocol on the linked page.

### 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)

**Balanced accuracy, 5 configurations.** Dot: the estimate; line: 95% interval; dashed line: chance level where the payload records one.

- Spectral ridge: 54.9% (53.2%–56.7%)
- CSP+LDA: 47.1% (43.8%–50.2%)
- LaBraM: 53.5% (51.4%–55.4%)
- CBraMod: 62.9% (59.6%–66.5%)
- EEGNet: 71.0% (65.0%–76.3%)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| Spectral ridge | Rest versus right-hand imagery | Balanced accuracy | 54.9% (53.2%–56.7%) | 10 |
| [CSP+LDA](https://bci.report/methods/csp-lda/) | Rest versus right-hand imagery | Balanced accuracy | 47.1% (43.8%–50.2%) | 10 |
| [LaBraM](https://bci.report/methods/labram/) | Rest versus right-hand imagery | Balanced accuracy | 53.5% (51.4%–55.4%) | 10 |
| [CBraMod](https://bci.report/methods/cbramod/) | Rest versus right-hand imagery | Balanced accuracy | 62.9% (59.6%–66.5%) | 10 |
| [EEGNet](https://bci.report/methods/eegnet/) | Rest versus right-hand imagery | Balanced accuracy | 71.0% (65.0%–76.3%) | 10 |

### Pretraining · fixed readout

Read with its protocol: [Does pretraining help?](https://bci.report/topics/does-pretraining-help/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | MI / rest · pretrained | Balanced accuracy | 53.5% | 10 |
| [LaBraM](https://bci.report/methods/labram/) | MI / rest · random initialization (mean) | Balanced accuracy | 55.9% | 10 |
| [CBraMod](https://bci.report/methods/cbramod/) | MI / rest · pretrained | Balanced accuracy | 62.9% | 10 |
| [CBraMod](https://bci.report/methods/cbramod/) | MI / rest · random initialization (mean) | Balanced accuracy | 56.5% | 10 |

### Pretraining · train-selected readout

Read with its protocol: [Does pretraining help?](https://bci.report/topics/does-pretraining-help/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | MI / rest · pretrained | Balanced accuracy | 52.9% | 10 |
| [LaBraM](https://bci.report/methods/labram/) | MI / rest · random initialization (mean) | Balanced accuracy | 56.5% | 10 |
| [CBraMod](https://bci.report/methods/cbramod/) | MI / rest · pretrained | Balanced accuracy | 62.7% | 10 |
| [CBraMod](https://bci.report/methods/cbramod/) | MI / rest · random initialization (mean) | Balanced accuracy | 54.9% | 10 |

### Pretraining · fixed classical control

Read with its protocol: [Does pretraining help?](https://bci.report/topics/does-pretraining-help/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| Log-covariance ridge | MI / rest | Balanced accuracy | 52.3% (48.5%–56.4%) | 10 |

## Source and licence

### ds003810

**Credit** Peterson et al. · OpenNeuro ds003810, version 2.0.2. Study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9114495/

[CC0-1.0 ↗](https://creativecommons.org/publicdomain/zero/1.0/) · [Dataset record ↗](https://doi.org/10.18112/openneuro.ds003810.v2.0.2)

BCI Report does not redistribute any recording. These are aggregate measurements computed by BCI Report under the licence above; the data belong to the people credited.

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

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Markdown copy of https://bci.report/datasets/ds003810/, generated from the published page. Figures are aggregate results; terms of use: https://bci.report/data-use/