![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359386/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359390/pasted-from-clipboard.png)
Brain Language Model
1
Massive data collection + Data pipeline
2
Build Baseline models for EEG
3
Build foundation model for EEG
- downstream tasks
- scaling laws
4
Foundational models for other Modalities (EEG, iEEG, MEG)
- Less noisy, more diverse
6
Foundational models for all modalities (MRI, PET, EEG, iEEG, MEG)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359394/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239258/pasted-from-clipboard.png)
5
Foundational Models for (MRI, PET)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239253/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239254/pasted-from-clipboard.png)
From Last week
- Build more suitable architectures for EEG
- Build more suitable objectives functions
- Build better fine-tuning strategy
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359455/pasted-from-clipboard.png)
FM
1
Massive data collection + Data pipeline
2
Build Baseline models for EEG
3
Build foundation model for EEG
- downstream tasks
- scaling laws
4
Foundational models for other Modalities (EEG, iEEG, MEG)
- Less noisy, more diverse
6
Foundational models for all modalities (MRI, PET, EEG, iEEG, MEG)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359394/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239258/pasted-from-clipboard.png)
5
Foundational Models for (MRI, PET)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239253/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239254/pasted-from-clipboard.png)
- LaBraM
- NEURO-GPT
- Others
Literature
drawbacks
- Fails to manage channel variability
- Less downstream tasks
- Not big performance improvements
- Only focus on one modality
Issues with EEG Data
- Extremely Noisy
- Variable number of channels
- Continues signal (i.e. not discrete - harder for modeling)
What we have achieved so far?
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Collected Massive EEG data:
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Openneuro Corpus
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Temple University Hospital (TUH) EEG corpus
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Cuban Human Brain Mapping Corpus and Others ...
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Over terabytes of data and thousands of hours of recordings
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Extreme diversity in subjects and sessions
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Implemented easy-to-plugin code
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Implemented a very efficient data pipeline
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Implemented baseline models wav2vec2 architecture for pretraining
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Tested various fine-tuning strategies (with MOABB benchmark)
Approaches used so far?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359449/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359454/Screenshot_2024-05-24_at_14.50.49.png)
Next steps
- Build more suitable architectures for EEG
- Build more suitable objectives functions
- Build better fine-tuning strategy
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11359455/pasted-from-clipboard.png)
Thank You!
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2687117/images/11239227/pasted-from-clipboard.png)
Questions?
Palette
By Jama Hussein Mohamud
Palette
- 51