Classification of Emotions using EEG Signals

Abstract

Emotion classification remains a challenging problem in affective computing. One of the most crucial areas of study in the field of brain wave research is the classification of emotions. Classifying the types of emotions accurately is one of the major issues with the analysis of brainwave emotion. EEG signals used for real-time emotion identification are crucial for affective computing and human-computer interaction. These signals can be produced by the user while engaging in a variety of cognitive, affective, and physical tasks, representing the functionality of the brain. The resulting emotional state produced gives valuable insights on the attitudes and actions of participants in specific situations. The main objective of this research work is to classify the emotions using EEG signals. The process is divided into two steps. The first step is feature extraction and the next step is classification. The feature extraction is performed by using DWT and the selection is done by using L1 norm. The algorithms used to perform signal classification are LSTM, GRU and DNN.