guangyizhangbci/PARSE
IEEE Transactions on Affective Computing, 2022
This project helps researchers and scientists in brain-computer interface (BCI) and neuroscience fields to automatically recognize emotions from brainwave (EEG) data. It takes raw or preprocessed EEG recordings from human subjects watching emotional stimuli and outputs classifications of their emotional state (e.g., positive, negative, neutral, happy, sad, fear). This tool is designed for BCI researchers, neuroscientists, and affective computing practitioners working with EEG data for emotion recognition.
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Use this if you are a BCI researcher or neuroscientist looking to accurately classify emotions from limited labeled EEG data, leveraging semi-supervised learning techniques.
Not ideal if you need a plug-and-play solution for non-EEG biosignals or if you lack expertise in data preprocessing and feature extraction for EEG.
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Python
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Dec 02, 2023
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