neerajwagh/eeg-self-supervision
Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability". Accepted at ML4H Symposium 2021 with an oral spotlight!
This project offers tools to improve how well machine learning models classify conditions based on EEG brainwave data. It takes raw or pre-processed EEG data as input and outputs more accurate and reliable classifications for tasks like identifying eye states, gender, or age from brain activity. This is intended for researchers and practitioners in neuroscience, neurology, and clinical settings who work with EEG data and want to build more robust diagnostic or predictive models.
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Use this if you are working with Electroencephalography (EEG) data and need to build machine learning models that can accurately classify specific conditions or attributes, even with limited labeled data.
Not ideal if your primary goal is to analyze raw EEG signals without focusing on automated classification, or if you are not working with machine learning models.
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23
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3
Language
Python
License
MIT
Category
Last pushed
Jul 12, 2023
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