View source for Pretraining Foundational Models for EEG Signal Analysis Using Open Source Large Scale Datasets

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[[File:LLM_EEG.png|thumb|right|300px]]
=== Introduction and Project Description===
Electroencephalography (EEG) stands as a pivotal non-invasive method for capturing the brain's electrical activity, playing an indispensable role in both neurological research and clinical diagnostics. It acts as a portal to the brain's complex mechanisms, providing crucial insights for diagnosing and treating a range of neurological conditions. Yet, the analysis of EEG signals is a complex endeavor, challenging due to the intricate nature of these signals and the nuanced differentiation required between normal and abnormal brain patterns.

This project is uniquely situated at the confluence of neuroscience and the rapidly evolving domain of artificial intelligence (AI). Its objective is to innovate in EEG signal analysis, addressing the nuanced challenges inherent in these signals. This initiative recognizes a significant void in AI applications: while foundation models have revolutionized areas like image and language processing, their impact on time-series data analysis, particularly for EEG, is yet to be fully realized.

Foundation models, known for their extensive pre-training on large datasets before being fine-tuned for specific tasks, have dramatically altered the landscape in fields like natural language processing and computer vision. Nevertheless, their application in interpreting the complexities inherent in EEG data is still in its nascent stages. This project proposes to develop a sophisticated foundational model specifically for EEG analysis, harnessing the capabilities of deep learning and AI.

This model represents a major advancement in the concept of 'AI for science,' where AI is not merely a tool for automation but a collaborative force in scientific exploration. By integrating advanced computational methods with rich EEG datasets, this model is designed to go beyond traditional EEG signal interpretation limits. Utilizing large-scale, publicly available EEG datasets, the model will undergo training to identify and analyze an extensive range of EEG patterns, covering both typical and atypical brain activities.

The project aims to address the existing limitations in EEG analysis, such as the reliance on labor-intensive manual interpretation and vulnerability to subjective bias. Through the automation of the analysis process and the establishment of standardized interpretation criteria, the foundational model is expected to markedly improve the efficiency and accuracy of EEG signal analysis, thereby reducing the time and effort required for precise diagnostics.

This initiative therefore seeks to pioneer a new approach in EEG signal analysis, developing an advanced, AI-based foundational model. This model, by effectively interpreting a wide array of EEG data and patterns, aims to enhance the accuracy and efficiency of EEG analysis, contributing significantly to advancements in neurological research and clinical diagnostics.

Your task:

* To develop a foundational model specifically tailored for EEG signal analysis: The model will be designed to capture the unique characteristics of EEG data.
* To leverage open-source datasets for pretraining: Utilize extensive, publicly available EEG datasets to train the model, ensuring a comprehensive and diverse data foundation.
* To evaluate the model's performance in real-world applications: Test the model's effectiveness in tasks such as anomaly detection, pattern recognition, and predictive analysis in EEG signals.

=== Methodology ===
* Dataset Compilation: Collect and preprocess open-source EEG datasets, ensuring data quality and diversity. This includes datasets from different demographics, conditions, and acquisition settings.
* Model Architecture Design: Design a neural network architecture suitable for EEG signal analysis, possibly incorporating elements from existing successful models in other domains.
* Pretraining: Train the model on the compiled dataset, employing transfer learning and unsupervised or semi-supervised learning methods.
* Fine-Tuning: Refine the model with a smaller, more specialized dataset for specific tasks (e.g., seizure detection, sleep stage classification).
* Validation and Testing: Evaluate the model's performance using standard metrics like accuracy, precision, recall, and F1-score. Perform comparative analysis with existing models.

=== Expected Outcomes ===
* A robust foundational model pre-trained on diverse EEG data, capable of adapting to various specific EEG analysis tasks.
* Enhanced accuracy and efficiency in EEG signal interpretation, contributing to better diagnostic and therapeutic approaches in neurology.
* A significant contribution to the field of biomedical signal processing, demonstrating the potential of foundational models in healthcare.


===Status: Available ===
:Looking for Master Project Students
:Supervision: [[:User:Thoriri | Thorir Mar Ingolfsson]], [[:User:Cosandre | Andrea Cossettini]], Yawei Li

===Your Profile===
*	Background in Engineering (Biomedical, Mechanical, Chemical…) 
*	Familiar with Deep Learning, experience with TensorFlow or PyTorch
*	Motivation to work on a project at the intersection of Neuroscience and Engineering
*	Willingness to work in an interdisciplinary team (Hardware, Machine Learning, Psychology, Neuroscience)

===Reach out===
If you are interested, we would love to get to know you! Please write an Email to all of us, including your motivation, CV, and transcript of records: 
* Thorir Mar Ingolfsson: thoriri@iis.ee.ethz.ch
* Andrea Cossettini: cossettini.andrea@iis.ee.ethz.ch
* Yawei Li: yawei.li@vision.ee.ethz.ch

===Professor===
: [http://www.iis.ee.ethz.ch/portrait/staff/lbenini.en.html Luca Benini]
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