Open-Source EEG Foundation Model - Ideas & Opinions

[FloofCat]

Hi @zeydabadi!

I'm Advik, an undergraduate from BITS Goa, keen to commit to this project and I'm particularly excited about this project's potential to create a readily accessible tool for EEG analysis.

I possess strong programming skills in Python, particularly in using the PyTorch and TensorFlow frameworks. My background includes experience with self-supervised learning and large language models, which I believe will be valuable assets in developing this foundation model. Additionally, I have a strong foundation in data analysis, creation of various novel (weird, but amazing) machine learning models, systems, explain-ability of AI which will aid in understanding and processing the EEG data.

So far, here's the progress I've had. I've read several papers regarding this field and explored several limitations and areas to work on. Here are a few papers and few things we could discuss:

• EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization - This paper is extremely novel and was really lovely to read. However, there are a few aspects that I found limiting when trying to explore the code of this paper. The paper describes using 1D temporal and spatial convolutions. The use of alternative convolutional architectures, like dilated convolutions or separable convolutions, might improve feature extraction. Furthermore, the paper utilizes a self-attention mechanism but doesn't delve deeply into the mathematical properties of this attention. Further analysis of the attention weights or visualization techniques could be mathematically explored to understand how the model focuses on relevant EEG information.
• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces - This was the most significant contribution to this field but there are again a few limitations that I could discover. More specifically, the paper mentions that the EEGNet architecture is tailored to specific BCI paradigms (e.g., motor imagery). This means it might not perform well on other EEG tasks requiring different signal characteristics without significant adjustments.

In essence, from these papers, we can conclude that we would like the following from our foundation model (since I'm more mathematically-oriented, apologies). Following are some approaches we could follow:

• Design a convolutional architecture that extracts both spectral and spatial features from EEG signals (either multi-channel convolutions or separate spectral and spatial convolutional layers).
• Maybe using VAEs or GANs to learn a latent representation of EEG data. This representation should capture underlying factors explaining the variability in unlabeled data. (?)

Apart from this, I think (since I work in similar fields) it would be great idea to incorporate these things along with this project:

• incorporating additional modalities alongside EEG data (something like eye-tracking data, MRI, and more - maybe HRV (?)). This could allow the model to capture a richer understanding of brain activity.
• integration of explainability techniques into the foundation model. This would help provide insights into the model's decision-making process for neuroscientists and clinicians - avoids one of the biggest challenges in these models and allows this model to be explainable in nature, while allowing for the use of black-box models (a self-attention or convolutional attention mechanism within the model architecture!).

Yep! Those are my thoughts so far! I've love to hear any opinions or questions from anyone - I'd love to further discuss this more below! I'm confident that my skills will enable me to make a significant contribution to this project. (apologies if I made this way too mathematically oriented, I love math :P)

Let me know what you guys think :)

• Advik (Bit)

[zeydabadi]

please include your email.

[FloofCat]

My email: f20221155@goa.bits-pilani.ac.in

Looking forward to hear from you!

[SumitdevelopAI]

Hello @zeydabadi

My name is Sumit Sharma, and I'm a B.tech CSE AI student at parul university with a strong interest in artificial intelligence and its potential applications in healthcare. I'm particularly fascinated by the emerging field of foundation models and their ability to unlock hidden patterns in complex datasets.

When I came across the GSOC 2024 project for developing an open-source EEG foundation model, I was immediately drawn to the potential impact it could have. The idea of leveraging a pre-trained model to analyze EEG data, especially in scenarios where specific task-related data is limited, aligns perfectly with my passion for using AI to address real-world medical challenges.

Project Understanding and My Interest:

I've been actively researching open-source foundation models, particularly those designed for medical data analysis. I'm impressed by the recent advancements in this area, and I believe there's significant room for further exploration, especially in the context of EEG data.

Currently, one of the biggest hurdles in utilizing EEG for various clinical applications is the scarcity of labeled data for specific tasks. Imagine a foundation model pre-trained on a vast repository of public EEG datasets, equipped to identify underlying patterns and features that might be missed in smaller, task-specific datasets. This could revolutionize early diagnosis, treatment monitoring, and potentially even open doors to personalized medicine approaches using EEG analysis.

My Skills and Contribution:

While I possess a strong foundation in programming (particularly Python) and experience with deep learning frameworks like PyTorch, I acknowledge that my knowledge of signal processing and neuroscience is still under development. However, I'm a highly motivated and fast learner. If given the opportunity to work on this project under your esteemed mentorship, I'm confident I can quickly grasp the necessary concepts and contribute meaningfully.

Here's where I would greatly benefit from your guidance:

Concept Development: I'm eager to discuss potential deep learning architectures and pre-training strategies suitable for EEG data analysis. Your expertise in this area would be invaluable in shaping the technical direction of the project.

Model Exploration: I'd welcome the opportunity to discuss methods for effective model exploration and potentially developing visualization tools to understand the model's learned representations

Dataset Selection: Identifying appropriate open-source EEG datasets is crucial. Your insights into publicly available, well-curated datasets aligned with this project's goals would be immensely helpful.

I look forward to the opportunity to collaborate with you.

Sumit Sharma