Difference between revisions of "Pretraining Foundational Models for EEG Signal Analysis Using Open Source Large Scale Datasets"

Revision as of 18:57, 14 December 2023

Introduction and Project Description

Electroencephalography (EEG), a non-invasive technique for recording electrical activity in the brain, plays a vital role in neurological research and clinical diagnostics. It serves as a window into the intricate workings of the brain, offering invaluable insights for the diagnosis and treatment of a spectrum of neurological disorders. However, the interpretation of EEG signals presents a multifaceted challenge, primarily due to their complex nature and the subtleties involved in distinguishing between normal and pathological patterns.

Recognizing these challenges, this project aims to pioneer a novel approach in analyzing EEG signals. The central proposition is developing an advanced foundational model, which will be pre-trained on an expansive array of open-source EEG datasets. This model, grounded in deep learning and artificial intelligence principles, seeks to transcend the conventional boundaries of EEG signal interpretation.

The proposed foundational model will integrate cutting-edge computational techniques with extensive EEG data, encompassing various brain states and conditions. By leveraging large-scale, open-source EEG datasets, the model will be trained to recognize and analyze a wide spectrum of EEG patterns, encompassing typical and atypical brain activities. This comprehensive training approach is designed to equip the model with an exceptional ability to discern subtle nuances in EEG signals, enhancing its diagnostic accuracy and reliability.

Furthermore, this project intends to address the prevalent limitations in current EEG analysis methodologies, such as the need for extensive manual interpretation and the susceptibility to subjective biases. By automating the analysis process and standardizing the interpretation criteria, the foundational model aims to significantly elevate the efficiency of EEG signal analysis, reducing the time and resources required for accurate diagnosis.

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: Thorir Mar Ingolfsson, 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

Luca Benini

↑ top