This Project in the GSOC[Google Summer of code] 2024 by Biomedical Informatics at Emory University. In this project we build foundation model of EEG [Electroencephalography].
Electroencephalography (EEG) data analysis, there's a growing recognition of the potential of foundation models—large-scale models pre-trained on extensive, often unlabeled data. Recent studies indicate that these models could significantly enhance the analysis of complex EEG patterns, particularly in scenarios where data availability for specific tasks is limited.
The EEG Foundation Model project is dedicated to harnessing this potential by creating an open-source foundation model tailored specifically for EEG data analysis. This ambitious initiative involves developing cutting-edge algorithms for EEG signal processing, automatic feature extraction, and deep learning-based pre-training on publicly available EEG datasets.
By leveraging the power of foundation models, our goal is to revolutionize EEG data analysis, unlocking new insights and capabilities that could lead to advancements in diagnostic accuracy, clinical decision support, and neuroscience research. Join us on this journey as we push the boundaries of EEG analysis and pave the way for transformative discoveries in brain science.
1 Literature Review: Research and review existing open-source foundation models for medical data, specifically EEG data. Identify the current limitations and challenges in this field.
2 A robust, open-source EEG foundation model.
3 Documentation and examples demonstrating the model's usage.
4 A report detailing the methodologies used and the performance of the model.
5 The model weights and code will be made publicly available on a platform such as GitHub.