justin-xzliu/GLIM
Official PyTorch implementation of research papaer "Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation".
This project helps neuroscientists and cognitive researchers translate raw electroencephalogram (EEG) signals directly into coherent, semantically faithful text. You input EEG recordings from subjects reading or processing information, and it outputs natural language sentences that summarize the core meaning of their brain activity. It's designed for researchers exploring brain decoding and brain-computer interfaces.
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Use this if you need to reliably generate descriptive text from EEG data, specifically to understand the semantic content of brain activity without concerns about text 'hallucinations'.
Not ideal if your primary goal is verbatim reconstruction of stimulus texts or if you are not working with EEG data for language processing.
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Jun 01, 2025
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