EEG Project Progress/Status/Next

How do achieve a better finetuning strategy?

Currently, given (B, T, C, D) we compress the signal via a sequence of layers

Are these the best approaches?

So far with end-2-end training Wav2vec2 + Shallownet achieves ~65 on single subject/single session but drops to ~45 with proper evaluation

Currently, given (B, T, C, D) we compress the signal via a sequence of layers

How about a BERT-like approach (with a CLS token)?
How about changing the objective function and removing convolutions.. i.e. only using a single transformer on raw input?
- With L2 loss objective?
We need a simple fine-tuning pipeline without adding extra complications

- Uses Temple University Hospital (TUH) EEG corpus

- selected 22 channels only

- Processed 20,000 (19,000 train/ 1000 val) EEG recordings (~5656 hrs)

- Split EEG signal into N chunks, each chunk has dim C * T.

- Each chunk is treated independent sample

Collected and pre-trained a 6M - 369M Transformer model on more than 2,500 hours of diverse EEG data
Handle EEG signals with various channels and time lengths
They evaluate four downstream tasks in BCI, they surpass all SOTA methods by a large margin
Train a neural tokenizer to discretize EEG signals into discrete neural tokens
During pre-training, part of EEG patches are masked while the objective is to predict masked tokens from visible patches.
Eval on 2 datasets, not a big margin as they claimed... relatively compared to architecture complexity.

- Introduce AdaCT, Adapters to convert time series data into spatio-temporal 2D pseudo-images or text.

- Then use pretrained models for text/image

- No involvement with EEG pretraining directly.

- Looks like Fake work (I didn't even get how the convert it to text)

By Jama Hussein Mohamud