braindecode/labram-pretrained

This model has been pushed to the Hub using the PytorchModelHubMixin integration:

• Code: https://braindecode.org
• Paper: [More Information Needed]
• Docs: https://braindecode.org/stable/generated/braindecode.models.Labram.html

Training data

The data that shaped these weights. The ids are the datasets: field of this card, which braindecode reads into model.card_data.datasets when you call from_pretrained (MOABB class names for MOABB datasets, OpenNeuro / NEMAR accessions, Hub ids otherwise). nm/on ids are datasets on NEMAR (on = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked private: are data the authors did not release; they are not in datasets:.

datasets: id	Dataset	Stage	Modality
BCIC-IV-1	BCI Competition IV dataset 1 (Blankertz et al. 2007; 59 ch, 7 subjects, motor imagery)	pretraining	EEG
Emobrain	Emobrain, eNTERFACE'06 multimodal emotion dataset (Savran et al. 2006; 64-ch EEG + fNIRS, 16 subjects)	pretraining	EEG
WAY-EEG-GAL	Grasp-and-Lift EEG Challenge (Luciw et al. 2014; 32 ch, 12 subjects)	pretraining	EEG
Inria-BCI-Challenge	Inria BCI Challenge / Kaggle ERN (Margaux et al. 2012; P300 speller error-related potentials, 26 subjects)	pretraining	EEG
on004362	EEG Motor Movement/Imagery Dataset (EEGMMIDB, Schalk et al. 2004; 109 subjects)	pretraining	EEG
Trujillo2020	Raw EEG Data (Trujillo 2020; information-integration and rule-based categorization tasks, 64 ch)	pretraining	EEG
Trujillo2017	Resting State EEG Data (Trujillo et al. 2017; 22 subjects, 64 ch)	pretraining	EEG
SEED	SJTU Emotion EEG Dataset (15 subjects)	pretraining	EEG
SEED-IV	SJTU Emotion EEG Dataset IV (15 subjects)	pretraining	EEG
SEED-GER	SEED-GER, German-subject emotion EEG (8 subjects; Liu et al. 2022)	pretraining	EEG
SEED-FRA	SEED-FRA, French-subject emotion EEG (8 subjects; Liu et al. 2022)	pretraining	EEG
nm000385	Siena Scalp EEG Database (Detti et al. 2020; 14 patients)	pretraining	EEG
SPIS-Resting-State	SPIS Resting State Dataset (Torkamani-Azar et al. 2020; 10 subjects)	pretraining	EEG
nm000216	Brain Invaders calibration-less P300 BCI with modulation of flash duration (bi2015a; Korczowski et al. 2019) - LaBraM's 'Target Versus Non-Target'	pretraining	EEG
TUAR	TUH EEG Artifact Corpus	pretraining	EEG
TUEP	TUH EEG Epilepsy Corpus	pretraining	EEG
TUSZ	TUH EEG Seizure Corpus	pretraining	EEG
TUSL	TUH EEG Slowing Corpus	pretraining	EEG
private:LaBraM-self-collected-EEG	LaBraM authors' self-collected EEG (>140 subjects, 62 ch, 1000 Hz, ESI NeuroScan; 342.23 h; not released)	pretraining	EEG

• same corpus for the vector-quantized neural tokenizer (vqnsp) and masked-code pretraining; 2,534.78 h total; TUAB, TUEV, SEED-V and MoBI (downstream) excluded per paper
• Incomplete list. Includes 342.23 h of private self-collected EEG that the paper does not itemise; the released checkpoint's exact cohort is not confirmed by the authors (issue #75). A dataset missing here is therefore not proof that these weights never saw it.
• Sources: arXiv:2405.18765 Appendix D (Pre-training Dataset Description); arXiv:2405.18765 Appendix D; arXiv:2405.18765 References; arXiv:2405.18765 Sec. 3 Experiments, paragraph 'Pre-training & Fine-tuning'.

Cite Braindecode

If you use this braindecode port in scientific work, cite the software on Zenodo, doi:10.5281/zenodo.17699192, and the original model and datasets cited above.

@software{braindecode,
  author = {Aristimunha, Bruno and
            Guetschel, Pierre and
            Wimpff, Martin and
            Gemein, Lukas and
            Rommel, Cedric and
            Banville, Hubert and
            Sliwowski, Maciej and
            Wilson, Daniel and
            Brandt, Simon and
            Gnassounou, Théo and
            Paillard, Joseph and
            {Junqueira Lopes}, Bruna and
            Sedlar, Sara and
            Moreau, Thomas and
            Chevallier, Sylvain and
            Gramfort, Alexandre and
            Schirrmeister, Robin Tibor},
  title = {Braindecode: toolbox for decoding raw electrophysiological brain data
           with deep learning models},
  url = {https://github.com/braindecode/braindecode},
  doi = {10.5281/zenodo.17699192},
  publisher = {Zenodo},
  license = {BSD-3-Clause},
}