Summary of Foundational Gpt Model For Meg, by Richard Csaky et al.
Foundational GPT Model for MEG
by Richard Csaky, Mats W.J. van Es, Oiwi Parker Jones, Mark Woolrich
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning techniques can be used to train unsupervised models on large amounts of unlabelled data, followed by fine-tuning on specific tasks. This approach has been successful for various types of data, including images, language, and audio, potentially improving performance in downstream tasks such as encoding or decoding brain data. However, limited progress has been made applying this approach to modelling brain signals like Magneto- and Electroencephalography (M/EEG). The paper proposes two classes of deep learning foundational models that can be trained using forecasting of unlabelled MEG data. A modified Wavenet model is considered, as well as a modified Transformer-based (GPT2) model with novel tokenisation and embedding methods for continuous multichannel time series data. The forecasting framework is extended to include condition labels as inputs, enabling better modelling of task data. Performance comparisons are made between these deep learning models and standard linear autoregressive (AR) modelling on MEG data. Results show that GPT2-based models provide better modelling capabilities than Wavenet and linear AR models, reproducing temporal, spatial, and spectral characteristics of real data and evoked activity in task data. The model scales well to multiple subjects while adapting to each subject through subject embedding. Finally, the paper demonstrates the potential usefulness of such a model in downstream decoding tasks through data simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using deep learning techniques to analyze brain signals like those measured by Magneto- and Electroencephalography (M/EEG). Normally, these signals are difficult to understand, but this approach could help improve our ability to decode what they mean. The researchers propose two new models that can be trained on large amounts of unlabelled data before being fine-tuned for specific tasks. One model is based on a technology called Wavenet, while the other uses a Transformer-based model with some special tricks for dealing with brain signals. They test these models and find that one of them, the GPT2 model, does a much better job than the others at capturing the patterns in the brain data. This could be useful for decoding what certain brain signals mean or even for diagnosing conditions like epilepsy. |
Keywords
» Artificial intelligence » Autoregressive » Deep learning » Embedding » Fine tuning » Time series » Transformer » Unsupervised
