ANALYSING EEG SIGNAL TO RECOGNISE EMOTIONS

Abstract

Emotion recognition through Electroencephalography (EEG) is widely acknowledged as a reliable method with diverse applications including defense, aerospace, and medical do- mains. This research aims to detect emotions from EEG signals, depicting individuals’ brain activity. Electroencephalogram (EEG) serves as a pivotal tool in Brain-Computer Interface (BCI) systems, capturing brain signals. With the advancement of machine learning algo- rithms and the growing real-world utility of BCIs, the classification of emotions from EEG data has gained prominence. Previously, researchers had limited insights and knowledge into the specific connections between distinct EEG characteristics and various emotional states. This work utilizes the DEAP dataset, featuring 32 EEG recording channels, and employs ensemble models for training. The model’s performance is evaluated using metrics like accu- racy, precision, recall, and F1-score. In this work, we augment traditional feature extraction methods such as mean, standard deviation, entropy, skewness, and kurtosis along with con- tinuous wavelet transformation for enhanced signal analysis, This combined approach aims to capture both basic characteristics and time-frequency domain information within the EEG signals, potentially improving emotion classification accuracy.