Exploratory Development of a Unified Foundational Model for Multi Biosignal Analysis

Introduction and Project Description

Biosignal analysis constitutes a cornerstone in contemporary healthcare and medical research, playing a crucial role in diagnosing, monitoring, and understanding various health conditions. In the realm of biosignal interpretation, diverse types of signals like electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG) provide unique insights into the body's physiological processes. Traditionally, the analysis of these biosignals has been conducted using specialized models tailored to each signal type. This specialized approach, while effective, often leads to compartmentalized and resource-intensive processes, requiring distinct methodologies and expertise for each type of biosignal.

Addressing these challenges, this project embarks on an ambitious venture to develop a unified foundational model. This innovative model aims to seamlessly process and interpret multiple types of biosignals concurrently. By transcending the conventional boundaries of biosignal analysis, the proposed model seeks to integrate the analysis of EEG, ECG, EMG, and potentially other biosignals into a cohesive framework.

The development of such a unified model presents numerous advantages. It promises to streamline the diagnostic process in clinical settings, allowing for a more comprehensive and holistic view of a patient's physiological state. In medical research, this approach can facilitate a more integrated understanding of the interactions between different physiological systems. For instance, simultaneously analyzing EEG and ECG data could yield novel insights into the interplay between brain and heart functions, potentially unveiling new correlations and patterns relevant to health and disease.

The technical foundation of this project will leverage the latest advancements in machine learning, particularly in deep learning and neural networks. By training the model on diverse datasets encompassing various biosignal types, it will develop the capability to recognize and interpret a wide range of physiological patterns. This training will involve not only individual signal analysis but also the exploration of inter-signal relationships and dependencies, which are often pivotal in understanding complex health conditions.

Moreover, the model will be designed with adaptability and scalability in mind, ensuring its applicability across different medical settings and patient demographics. This aspect is particularly crucial in catering to the diverse nature of biosignals across individuals, which can vary due to factors like age, gender, health status, and environmental influences.

Your task:

• To investigate the feasibility of a unified model for multiple biosignals: Explore the potential and challenges in combining EEG, ECG, EMG, and other biosignals into a single model.
• To develop a prototype of a foundational model for multi-biosignal analysis: Create an initial version of the model using available datasets and evaluate its performance.
• To document the exploratory process and findings: Provide a detailed account of the methodologies, challenges, and insights gained during the project.

Methodology

• Literature Review and Conceptual Framework: Conduct an extensive review of existing models for individual biosignals and identify potential strategies for integration.
• Dataset Compilation: Gather and preprocess diverse biosignal datasets to train the model, focusing on interoperability and data quality.
• Model Architecture Development: Design an innovative model architecture that can process and analyze multiple biosignals simultaneously.
• Experimental Training and Testing: Train the model with the compiled datasets and test its performance in various analytical tasks.
• Analysis and Documentation: Analyze the results, document the challenges encountered, and propose recommendations for future research.

Expected Outcomes

• A comprehensive understanding of the challenges and potential strategies in developing a unified model for multi-biosignal analysis.
• A prototype model that provides initial insights into the feasibility of this approach.
• A detailed thesis documenting the exploratory process, findings, and recommendations for future research in this field.

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

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