EEG-Based Human Affective State Analysis for Emotion Recognition - Chunawale, Abhishek; Bedekar, Mangesh; Bhalekar, Madhuri; Girase, Sheetal (Engineering, Technology & Applied Science Research, 2026)
The human brain signals generated during emotional activities can be recorded and analyzed with the use of Electroencephalography (EEG) to understand the exact state of the brain. EEG has influenced the field of affective computing research since it can classify human emotions more precisely than facial expression, text, body gestures, or audio signal recognition. Emotion classification utilizing machine learning includes the selection and extraction of various emotion-related features taken from the EEG signals. This study addresses the challenge of proper feature selection and accurate emotion classification based on real-time EEG data. The proposed system considers frequency and wavelet domain features. The Random Forest (RF) classifier gave the highest accuracy of 97% for classifying four emotions, namely happiness, sadness, calm, and stress. K-Nearest Neighbors (KNN) performed quite well with 94% accuracy followed by Decision Tree (DT) with 93% accuracy. Wavelet domain features had 89% accuracy with RF, outperforming Support Vector Machine (SVM), KNN, Gradient Boosting (GB) and Decision Tree.