ricsinaruto/MEG-transfer-decoding
Explore the differences between sliding window and full-epoch models on MEG data and use PFI to uncover neuroscientific insights.
This project helps neuroscientists analyze Magnetoencephalography (MEG) data to understand how the brain decodes visual stimuli. It takes raw or preprocessed MEG data and applies different classification models to determine which brain activity patterns correspond to specific stimuli. Researchers in cognitive neuroscience can use this to compare modeling approaches and extract interpretable brain features.
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Use this if you are a neuroscientist researching visual decoding from MEG data and want to compare full-epoch versus sliding-window models or extract interpretable features using permutation feature importance.
Not ideal if you are working with group-level MEG data analysis or require a robust, production-ready decoding pipeline without expecting to troubleshoot.
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MIT
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Last pushed
May 21, 2024
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