# LaBraM: results on public EEG datasets

Foundation model

LaBraM (Large Brain Model) is an EEG foundation model from Jiang, Zhao and Lu (Shanghai Jiao Tong University; ICLR 2024), described in Base, Large and Huge sizes. A vector-quantized tokenizer turns per-channel EEG patches into discrete codes, and a Transformer is pretrained to predict the codes of masked patches, using about 2,500 hours of EEG from around 20 datasets.

**Also known as** LaBraM · Large Brain Model · LaBraM-Base · Labram · Jiang et al. 2024

**Reference** [Jiang W-B, Zhao L-M, Lu B-L. Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. International Conference on Learning Representations (ICLR), 2024. ↗](https://arxiv.org/abs/2405.18765)

Directory status: Evaluated · [Official code ↗](https://github.com/935963004/LaBraM) · Description sources [arxiv.org](https://arxiv.org/abs/2405.18765) · [github.com](https://github.com/935963004/LaBraM) · [github.com](https://github.com/braindecode/braindecode/blob/v1.5.1/braindecode/models/labram.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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Rest versus right-hand imagery | Balanced accuracy | 53.5% (51.4%–55.4%) | 10 |

### [ds003810](https://bci.report/datasets/ds003810/) · 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 |

### [ds003810](https://bci.report/datasets/ds003810/) · 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 |

### [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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Serial subtraction versus resting EEG | Balanced accuracy | 64.6% (60.5%–68.6%) | 36 |

### [EEGMAT](https://bci.report/datasets/eegmat/) · 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 |

### [EEGMAT](https://bci.report/datasets/eegmat/) · 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 |

### [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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | 40 visual targets · 8 posterior electrodes | Balanced accuracy | 10.8% (9.5%–12.2%) | 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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | 40 visual targets · 4 posterior electrodes | Balanced accuracy | 12.9% (11.2%–14.6%) | 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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Visual target versus nontarget events | Balanced accuracy | 49.4% (47.7%–51.2%) | 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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Reading · target versus nontarget events | Balanced accuracy | 53.2% (51.3%–54.9%) | 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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Five stages · balanced scalp-EEG sample | Balanced accuracy | 71.1% (69.0%–73.2%) | 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 |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Seated motor imagery · cue-gated replay | Command detection ≤3 s | 18.3% | 4 |
| [LaBraM](https://bci.report/methods/labram/) | Seated motor imagery · cue-gated replay | Idle false activation | 3.3% | 4 |

### [Mobile BCI dataset (SSVEP and ERP paradigms)](https://bci.report/datasets/mobile-bci/) · Movement · SSVEP, 2-second windows

Read with its protocol: [On the move](https://bci.report/topics/on-the-move/) · [deployment-topics.json](https://bci.report/data/deployment-topics.json)

**Balanced accuracy, 3 configurations.** Dot: the estimate; line: 95% interval; dashed line: chance level where the payload records one.

- LaBraM · Standing · scalp · 8 ch: 59.5% (53.6%–65.5%)
- LaBraM · Slow walk · 0.8 m/s · scalp · 8 ch: 54.7% (49.5%–60.1%)
- LaBraM · Fast walk · 1.6 m/s · scalp · 8 ch: 47.8% (43.3%–52.3%)

| Method | Condition | Metric | Value | People |
| --- | --- | --- | --- | --- |
| [LaBraM](https://bci.report/methods/labram/) | Standing · scalp · 8 ch | Balanced accuracy | 59.5% (53.6%–65.5%) | 23 |
| [LaBraM](https://bci.report/methods/labram/) | Slow walk · 0.8 m/s · scalp · 8 ch | Balanced accuracy | 54.7% (49.5%–60.1%) | 23 |
| [LaBraM](https://bci.report/methods/labram/) | Fast walk · 1.6 m/s · scalp · 8 ch | Balanced accuracy | 47.8% (43.3%–52.3%) | 23 |

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

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