# EEGMAT: EEG decoding results

Mental arithmetic / rest

EEGMAT (EEG During Mental Arithmetic Tasks) is a PhysioNet database, version 1.0.0 published December 2018, with two 60-second artifact-free EEG recordings from each of 36 participants: one before and one during a serial-subtraction task. Contributed from Igor Sikorsky Kyiv Polytechnic Institute and described by Zyma et al. in Data (2019).

**Also known as** EEGMAT · EEG During Mental Arithmetic Tasks · Electroencephalograms during Mental Arithmetic Task Performance · Zyma et al. (2019)

Description sources [physionet.org](https://physionet.org/content/eegmat/1.0.0/) · [doi.org](https://doi.org/10.3390/data4010014)

## 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 · 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)

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

- Spectral ridge: 56.8% (54.6%–58.9%)
- LaBraM: 64.6% (60.5%–68.6%)
- CBraMod: 62.3% (58.1%–66.5%)
- EEGNet: 67.6% (62.8%–72.5%)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| Spectral ridge | Serial subtraction versus resting EEG | Balanced accuracy | 56.8% (54.6%–58.9%) | 36 |
| [LaBraM](https://bci.report/methods/labram/) | Serial subtraction versus resting EEG | Balanced accuracy | 64.6% (60.5%–68.6%) | 36 |
| [CBraMod](https://bci.report/methods/cbramod/) | Serial subtraction versus resting EEG | Balanced accuracy | 62.3% (58.1%–66.5%) | 36 |
| [EEGNet](https://bci.report/methods/eegnet/) | Serial subtraction versus resting EEG | Balanced accuracy | 67.6% (62.8%–72.5%) | 36 |

### 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/) | Mental workload · pretrained | Balanced accuracy | 64.6% | 36 |
| [LaBraM](https://bci.report/methods/labram/) | Mental workload · random initialization (mean) | Balanced accuracy | 56.5% | 36 |
| [CBraMod](https://bci.report/methods/cbramod/) | Mental workload · pretrained | Balanced accuracy | 62.3% | 36 |
| [CBraMod](https://bci.report/methods/cbramod/) | Mental workload · random initialization (mean) | Balanced accuracy | 56.4% | 36 |

### 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/) | Mental workload · pretrained | Balanced accuracy | 64.4% | 36 |
| [LaBraM](https://bci.report/methods/labram/) | Mental workload · random initialization (mean) | Balanced accuracy | 56.5% | 36 |
| [CBraMod](https://bci.report/methods/cbramod/) | Mental workload · pretrained | Balanced accuracy | 68.4% | 36 |
| [CBraMod](https://bci.report/methods/cbramod/) | Mental workload · random initialization (mean) | Balanced accuracy | 61.7% | 36 |

### 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 | Mental workload | Balanced accuracy | 61.9% (56.2%–67.6%) | 36 |

## Source and licence

### EEGMAT

**Credit** Igor Zyma, Ivan Seleznov, Anton Popov, Mariia Chernykh, Oleksii Shpenkov · EEG During Mental Arithmetic Tasks 1.0.0, PhysioNet, doi:10.13026/C2JQ1P. Study: Zyma et al. (2019), doi:10.3390/data4010014. PhysioNet platform: Pollard et al. (2026), doi:10.1038/s44360-026-00096-z.

[Open Data Commons Attribution License 1.0 ↗](https://opendatacommons.org/licenses/by/1-0/) · [Dataset record ↗](https://physionet.org/content/eegmat/1.0.0/)

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