Abstract

Emotion identification and categorization have been emerging in the brain machine interface in the current era. Audio, visual, and electroencephalography (EEG) data have all been shown to be useful for automated emotion identification in a number of studies. EEG-based emotion detection is a critical component of psychiatric health assessment for individuals. If EEG sensor data are collected from multiple experimental sessions or participants, the underlying signals are invariably non-stationary. As EEG signals are noisy, non-stationary, and non-linear, creating an intelligent system that can identify emotions with good accuracy is challenging. Many researchers have shown evidence that EEG brain waves may be used to determine feelings. This study introduces a novel automated emotion identification system that employs deep learning principles to recognize emotions through EEG signals from computer games. EEG data were obtained from 28 distinct participants using 14-channel Emotive Epoc+ portable and wearable EEG equipment. Participants played four distinct emotional computer games for five minutes each, with a total of 20 min of EEG data available for each participant. The suggested framework is simple enough to categorize four classes of emotions during game play. The results demonstrate that the suggested model-based emotion detection framework is a viable method for recognizing emotions from EEG data. The network achieves 99.99% accuracyalong with less computational time.

Highlights

  • Academic Editor: Stefano MarianiEmotions are one of humanity’s most distinguishing characteristics; they influence a person’s behavior [1]

  • The “DEEPHER” deep learning model consists of three long short-term memory recurrent neural network (LSTM) layers of 512,1024, and 2048 neurons, as well as a fully connected embedding layer, one dropout layer, and a dense layer

  • We describe previously short-term f1-Score memory recurrent neural network (LSTM) architecture for the task of in-depth emotion recognition

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Summary

Introduction

Emotions are one of humanity’s most distinguishing characteristics; they influence a person’s behavior [1]. Understanding and studying human emotions is a crucial aspect of existence. Automatic emotion categorization using machine learning and artificial intelligence has recently sparked interest, as it may be utilized in a range of human–computer interface (HCI) applications [2,3,4]. An artificial emotional intelligence system must have a thorough understanding of human emotional understanding and the connection between affective expression and emotions [5]. HCI systems must be capable of gaining a complete grasp of various human emotions and emotional expressions. Human ideas and emotions can be communicated verbally or nonverbally. HCI systems must be able to recognize, interpret, and evaluate nonverbal human expressions.

Related Work
Selected Data Set and Participants
EEG Signal Preprocessing
EEG Signal Classification Method
Model Complexity
Findings
Confusion
Conclusions and Future Work

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