EEG-based emotion recognition using hybrid CNN and LSTM classification

Abstract

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

Keywords: deep learning; electroencephalography; emotion recognition; machine learning; neural networks.

FIGURE 1

The flow of electroencephalography (EEG) data processing.

FIGURE 2

Overview of the methodology.

FIGURE 3

Architecture diagram for the methodology.

FIGURE 4

Convolutional neural network overview.

FIGURE 5

Training and validation loss.

FIGURE 6

Training and validation accuracy.

FIGURE 7

Bar chart representation of results.