Abhishek-Iyer1/emotion-classifcation-eeg-seed-ensemble
Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble
This project helps researchers and scientists in neuroscience or psychology analyze brainwave data to automatically detect and classify human emotions. You input raw or pre-processed EEG signals, and the system outputs a classification of the emotion (e.g., happy, sad, neutral). It's designed for anyone working with electroencephalography data who needs to accurately identify emotional states.
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Use this if you are performing research involving emotional responses and need an automated, highly accurate method to classify emotions from EEG brainwave recordings.
Not ideal if you are working with other forms of biometric data for emotion detection, such as facial expressions or voice analysis.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 26, 2021
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