Emotion Detection Based on EEG Signal Processing by Body Sensor 5G Networks Using Deep Learning Architectures

Abstract

Emotion recognition is the automatic detection of a person’s emotional state through his or her non-physiological or physiological signals. The EEG-related technique was an effectual system, which is typically employed for recognizing feelings in real time. Artificial Intelligence (AI) can be a developing research field which had rapid growth particularly to constitute a bridge between technology and its implementation in solving real-time issues particularly those relevant to the healthcare domain. This study develops a new deep learning-based emotion detection based on EEG signal processing, named DLED-EEGSP technique. The presented DLED-EEGSP technique identifies the distinct kinds of emotions based on the sensors and EEG signals. To perform this, the presented DLED-EEGSP technique exploits multi-head attention based long short-term memory (MHA-LSTM) method for emotion recognition. The MHALSTM model recognizes the emotion states based on the higher order cross feature samples. The experimental result analysis of the DLED-EEGSP technique is investigated on a series of data. A wide-ranging simulation results reported the supremacy of the DLED-EEGSP technique over other existing models.