Témakiírások
Healthcare-Driven Brain-Computer Interfaces: Deep Learning for Decoding Neural Communication from Brain Signals
témakiírás címe
Healthcare-Driven Brain-Computer Interfaces: Deep Learning for Decoding Neural Communication from Brain Signals
doktori iskola
témakiíró
tudományág
témakiírás leírása
Overview
Imagine a world where brain signals can be directly translated into meaningful communication, giving a voice to people who cannot speak. This PhD project invites you to contribute to that vision by applying advanced deep learning techniques to decode neural communication from brain signals such as EEG and ECoG.
The research will explore how neural networks can learn complex patterns in brain activity, reconstruct expressive features, and push forward the boundaries of communication-enabled Brain-Computer Interfaces (BCIs). You will investigate both accuracy and generalization across brain signal modalities, with potential applications in assistive communication technologies, neurorehabilitation, and healthcare innovation.
Objectives
1. Develop and test deep learning models for decoding neural communication from EEG and ECoG signals.
2. Compare performance with traditional decoding approaches to demonstrate the added value of deep learning.
3. Explore generalization across different brain-signal types and datasets.
4. Contribute to bridging neuroscience and AI by interpreting results in the context of real-world BCI systems.
Methodology
1. Design and implement deep learning architectures tailored for decoding neural communication.
2. Use open-source EEG/ECoG datasets and possibly collaborate with ongoing data-collection projects.
3. Evaluate models on accuracy, robustness, and generalization.
4. Disseminate results through high-impact journals and international conferences.
Potential Impact
This research has the potential to restore communication abilities for individuals with severe impairments, while also advancing the scientific understanding of how brain signals encode language and interaction. Your work could lay the foundation for the next generation of AI-driven, healthcare-focused BCIs.
Imagine a world where brain signals can be directly translated into meaningful communication, giving a voice to people who cannot speak. This PhD project invites you to contribute to that vision by applying advanced deep learning techniques to decode neural communication from brain signals such as EEG and ECoG.
The research will explore how neural networks can learn complex patterns in brain activity, reconstruct expressive features, and push forward the boundaries of communication-enabled Brain-Computer Interfaces (BCIs). You will investigate both accuracy and generalization across brain signal modalities, with potential applications in assistive communication technologies, neurorehabilitation, and healthcare innovation.
Objectives
1. Develop and test deep learning models for decoding neural communication from EEG and ECoG signals.
2. Compare performance with traditional decoding approaches to demonstrate the added value of deep learning.
3. Explore generalization across different brain-signal types and datasets.
4. Contribute to bridging neuroscience and AI by interpreting results in the context of real-world BCI systems.
Methodology
1. Design and implement deep learning architectures tailored for decoding neural communication.
2. Use open-source EEG/ECoG datasets and possibly collaborate with ongoing data-collection projects.
3. Evaluate models on accuracy, robustness, and generalization.
4. Disseminate results through high-impact journals and international conferences.
Potential Impact
This research has the potential to restore communication abilities for individuals with severe impairments, while also advancing the scientific understanding of how brain signals encode language and interaction. Your work could lay the foundation for the next generation of AI-driven, healthcare-focused BCIs.
felvehető hallgatók száma
1 fő
helyszín
TMIT
jelentkezési határidő
2026-01-15