Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.

Ala Hag,Fares Al-Shargie,Maryam Altalhi,Thulasyammal Pillai,Dini Handayani,Teddy Mantoro,Mun Hou Kit

doi:10.3390/s21248370

Abstract

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

Highlights

Mental stress is one of the apprising issues globally, which affects almost everyone
We report the mental stress state classification based on optimal feature set, selected from multi-domain features, of network connectivity features, time domain, frequency domain, and time-frequency domains, using the proposed method minimum redundancy maximum relevance (mRMR)-particle swarm optimization (PSO)-support vector machine (SVM)
A hybrid feature selection method, mRMR-PSO-SVM, was proposed to select the most informative features related to the mental stress task

Summary

Introduction

Mental stress is one of the apprising issues globally, which affects almost everyone. It is considered one of the major contributing causes to various serious health issues. Objective methods assessment such as EEG is considered one of the promising tools for building real-life applications, helping individuals assess themselves without the need for experts’ involvement. Building such an application needs an efficient method for EEG analysis, such as employing the most related channels and features to the mental state task. Feature selection and channel selection methods play an essential role in enhancing the classification performance, reducing system complexity, and increasing diagnoses’ convertibility [2]

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Conclusion