xiaobaben/BrainUICL
[ICLR 2025] BrainUICL: An Unsupervised Individual Continual Learning Framework for EEG Applications
This project helps neuroscientists, clinicians, and researchers working with EEG data to continuously improve brain signal analysis for individual patients or subjects. It takes existing EEG recordings from various individuals and helps a pre-trained model learn to interpret new, unseen individual brain patterns without forgetting previous knowledge. The output is a more accurate and adaptable model for tasks like classifying sleep stages or decoding brain-computer interface signals.
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Use this if you need to continually adapt an EEG analysis model to new individuals or patient data over time, while ensuring it retains its understanding of previously learned patterns.
Not ideal if your EEG analysis only involves a single, fixed dataset or if you're not working with individual-specific model adaptation over time.
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Language
Python
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Last pushed
Jul 19, 2025
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