SeyedMuhammadHosseinMousavi/Synthetic-Data-Generation-Algorithms
Synthetic Data Generation Algorithms (VAE-GAN-Diffusion Model-LSTM-Copula)
This project helps researchers and practitioners in affective computing expand their datasets for emotion recognition. It takes raw brainwave EEG data, specifically physiological signals tied to various emotional states, and generates additional synthetic EEG data. This expanded dataset can then be used to train and test machine learning models for improved robustness and generalization in emotion recognition tasks.
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Use this if you are working with physiological EEG data for emotion recognition and need to augment your existing dataset with realistic synthetic samples to improve model training and validation.
Not ideal if you are working with non-sequential physiological data or if your primary goal is not to enhance emotion recognition models.
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Language
Python
License
CC0-1.0
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Last pushed
Nov 07, 2024
Commits (30d)
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