Project DREAM aims to develop a scalable foundation model for
multi-channel EEG signal analysis, spanning from basic polysomnography
to high-density and intracranial recordings. The goal is to create a
unified AI model capable of adapting to various EEG configurations while
maintaining simplicity, interpretability, and computational efficiency.
By leveraging convolutional neural networks and attention mechanisms,
the model will extract robust features for diverse tasks, including
sleep stage classification, epilepsy detection, and cognitive function
assessment. Collaborations with leading hospitals will provide
real-world datasets for validation, ensuring the model’s generalization.
Ultimately, this project seeks to advance AI-driven biomedical signal
processing, enhancing diagnostic and monitoring capabilities in
neurology and sleep medicine.