This project focuses on classifying textual data into six distinct emotions using transformer-based models. It includes data preprocessing, model training, evaluation, and a user-friendly Streamlit ...

Loading and preprocessing ECG data for emotion classification. Handling class imbalance using SMOTE (Synthetic Minority Over-sampling Technique). Implementation of machine learning classifiers, ...

Emotion detection from electroencephalogram (EEG) signals holds immense potential for understanding human affective states and has wide-ranging applications in various domains. This ...