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The IUP Journal of Telecommunications

Accurate EEG-Based Emotion Detection Using Feature Optimization and Machine Learning Algorithm

Article Details

Pub. Date	: Aug' 2023
Product Name	: The IUP Journal of Telecommunications
Product Type	: Article
Product Code	: IJTC030823
Author Name	: Nayana Vaity and Ankit Temurnikar
Availability	: YES
Subject/Domain	: Arts & Humanities
Download Format	: PDF Format
No. of Pages	: 13

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Abstract

This paper proposes a feature optimization and detection method based on the bee scout algorithm and derived support vector machine (DSVM). The DSVM approach reduces network training time by removing unused features. First, the raw EEG data is decomposed using discrete wavelet transform (DWT) into a sequence of frequencies. Before the feature extraction procedure, the spatiotemporal component of the decomposed EEG signal is represented as a twodimensional spectrogram using the shifting. To extract features, four pre-trained SVMs are employed. Dimensional reduction and feature selection are accomplished by bee scout-based EEG channel selection and DSVM approach. The proposed algorithm is tested on MAHNOB dataset. The results suggest that the proposed algorithm is more efficient than existing algorithms.

Introduction

The advancement of machine learning (ML) and optimization algorithms has enhanced the accuracy of emotion detection in human-computer interfaces-the representation of emotion by humans as facial expression, textual speech and posture (Asghar et al., 2021; Joshi and Ghongade, 2021; Kimmatkar and Babu, 2021; Liu and Fu, 2021; and Ozel and Akan, 2021). The human brain's processing depends on the activity of electrical signals and the active task of execution. The task of execution by the human brain controls all the physical components of the human body. Electroencephalogram (EEG) is recorded signals of the human brain, and the collection of signals is very