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)

5

Foundational Models for (MRI, PET)

From Last week

  • Build more suitable architectures for EEG
  • Build more suitable objectives functions
  • Build better fine-tuning strategy

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)

5

Foundational Models for (MRI, PET)

  • 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?

  • Collected Massive EEG data:

    • Openneuro Corpus

    • Temple University Hospital (TUH) EEG corpus

    • Cuban Human Brain Mapping Corpus and Others ...

  • Over terabytes of data and thousands of hours of recordings

  • Extreme diversity in subjects and sessions

  • Implemented easy-to-plugin code ¬†

  • Implemented a very efficient data pipeline

  • Implemented baseline models wav2vec2 architecture for pretraining

  • Tested various fine-tuning strategies (with MOABB benchmark)

Approaches used so far?

Next steps

  • Build more suitable architectures for EEG
  • Build more suitable objectives functions
  • Build better fine-tuning strategy

Thank You!

Questions?

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