Human Emotion Recognition Based on EEG Signal Using Deep Learning Approach

Abstract

Nowadays, emotion recognition and classification plays a vital role in the field of human–computer interaction (HCI). Emotions are being recognized through body behaviors such as facial expression, voice tone, and body movement. The present research considers electroencephalogram (EEG) as one of the foremost used modality to identify emotions. EEG measures the electrical activities of the brain through a bunch of electrodes placed on the scalp. This mechanism is used due to its high temporal resolution with no risks and less cost. Over the last decades, many researchers involved EEG signals in sequence to cope up with brain-computer interface (BCI) and to detect emotions. It includes removing artifacts from EEG signals, extracting temporal or spectral features from the EEG signals, analysis on time or frequency domain, respectively, and eventually, designing a multi-class classification strategy. The paper discusses the approach of identifying and classifying human emotions based on EEG signals. The approach used deep learning technique such as long-short term memory (LSTM) model and gated recurrent units (GRUs) model for classification. The obtained experimental result seems to be promising with good accuracy in the emotion classification.