Decoding Emotions from EEG Responses Elicited by Videos using Machine Learning Techniques

Electroencephalography (EEG) has gained prominence as a non-invasive and efficient technique for decoding human emotional states based on neural activity. This paper presents a structured machine learning-based framework for emotion classification using EEG signals elicited by emotionally stimulating videos. Leveraging the SEED dataset, the study focuses on extracting meaningful features from multi-channel EEG recordings and classifying emotional states into positive, neutral, and negative categories. The methodology integrates advanced signal preprocessing, dimensionality reduction, and feature extraction techniques such as Differential Entropy (DE), Power Spectral Density (PSD), and Hjorth parameters. Machine learning classifiers including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Multi-Layer Perceptrons (MLP) are employed and compared for performance. Among these, the MLP model demonstrated superior accuracy, capturing nonlinear patterns effectively across EEG channels. Model evaluation is conducted using standard metrics such as accuracy, F1-score, and confusion matrix. The results confirm that the proposed EEG-based framework achieves robust emotion classification with significant implications for mental health monitoring, affective computing, and human-computer interaction applications.