Fusion of fMRI and EEG signals builds a multi-scale decoding model to improve the control accuracy of paralyzed patients.
Combining deep learning and signal processing for advanced movement interpretation in paralyzed patients.
Exceptional Quality and Innovation
Innovative Research Design Solutions
Combining signal processing and deep learning for advanced movement decoding in paralyzed patients.
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Proven Success
Innovative Research Solutions
We specialize in advanced research design integrating signal processing and deep learning techniques.
Multi-Stage Data Collection
Collect synchronized fMRI and EEG data from paralyzed patients for movement analysis.
Neural Architecture Development
Train novel multi-scale neural networks to decode intended movements effectively.
Contextual Pattern Interpretation
Incorporate fine-tuned GPT-4 for enhanced understanding across different data modalities.
Neural Movement
Innovative research combining signal processing and deep learning techniques.
Multi-Scale Approach
Our project utilizes synchronized fMRI and EEG data for decoding intended movements through advanced neural architectures and fine-tuned GPT-4 components.
Contextual Patterns
We interpret contextual patterns across modalities to enhance movement accuracy, response time, and stability metrics in paralyzed patients performing imagined movement tasks.