rkobler/TSMNet
Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
This project helps neuroscientists and researchers in brain-computer interfaces adapt machine learning models to new individuals or recording sessions when analyzing Electroencephalography (EEG) data. You input raw EEG data from different subjects or sessions, and it provides an improved classification model that performs well across these varying conditions. It's designed for someone working with EEG signals who needs to build robust predictive models despite individual or session-specific differences.
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Use this if you are developing or evaluating machine learning models for EEG data and need to ensure they perform consistently across different people or recording sessions without extensive manual re-calibration.
Not ideal if your primary focus is on other types of neuroimaging data like fMRI, or if you are looking for general-purpose EEG signal processing tools rather than domain adaptation for classification tasks.
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63
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Language
Python
License
BSD-3-Clause
Category
Last pushed
Oct 12, 2022
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