Description

Project Title

Brain Fingerprinting Using MEG Signals: Intrinsic Geometry and 

Deep Learning

General Description
This project explores the concept of individual “brain-prints” — unique neural signatures extracted from MEG (Magneto-encephalo-graphy) recordings. The goal is to investigate whether intrinsic geometric properties of brain connectivity patterns can be used for reliable human identification. The project combines modern signal processing, graph-based representations, and deep learning techniques to analyze large-scale open MEG datasets. Students will study how variations in brain dynamics reflect subject-specific characteristics and how these can be captured using advanced mathematical and machine learning tools.

Project Scope
The main objective of this project is to develop a framework for identifying individuals based on their MEG brain activity. Students will explore intrinsic geometric representations of functional brain networks and apply deep neural networks to learn discriminative subject-specific patterns. The study will focus on comparing geometric graph-based features with deep learning-based embeddings and evaluating their effectiveness in identification tasks. The project also aims to provide practical experience in neuro-signal processing, data-driven modeling, and biometrics.
Student Requirements
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Teamwork
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Full attendance in weekly meetings
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High motivation
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Independent learning
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Personal responsibility
Development Tools
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Python
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NumPy / SciPy
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MNE-Python
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PyTorch / TensorFlow
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Graph Signal Processing libraries
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Jupyter Notebook
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GitHub for version control
Deliverables
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Specification document
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YouTube video presenting the project
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Scientific poster
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Final presentation
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GitHub link to the project code
Additional Notes
This project will be conducted in collaboration with the Brain, Language, Inference, and Thought (BLIT) laboratory at Waseda University, Japan.
    
    
*The project is conducted in English.

Brain Fingerprinting Using MEG Signals

AI and Machine Learning

Description

Emphasis in project execution

The project is has cooperation with the industry and combines meeting deadlines while being creative and focused on the task