Impact of Pairwise Electrode Features in the Classification of Emotions for EEG Signal Analysis

Abstract

Emotion recognition is the capacity to recognize and interpret an individual's emotional state through a variety of techniques, one of which is the detection and interpretation of patterns of brain activity linked to specific emotional states. Applications for emotion recognition are numerous and include human-computer interaction, marketing research, and mental health diagnosis. Electroencephalography (EEG) signals are another name for the patterns of brain activity. To extract features from EEG waves, many techniques have been used. The wavelet transform (WT), differential entropy (DE), statistical features (SF), and convolutional neural network (CNN) are some of the feature extraction techniques performed. This proposed method utilizes a custom CNN model to train and test on the preprocessed SEED data.