Stress Detection from Different Environments for VIP Using EEG Signals and Machine Learning Algorithms

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

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Mental Stress Detection in University Students using Machine Learning Algorithms

Procedia Computer Science ◽

10.1016/j.procs.2019.05.007 ◽

2019 ◽

Vol 152 ◽

pp. 349-353 ◽

Cited By ~ 5

Author(s):

Ravinder Ahuja ◽

Alisha Banga

Keyword(s):

Machine Learning ◽

University Students ◽

Mental Stress ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Stress Detection

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Remote sensing machine learning algorithms in environmental stress detection: Case study of Pan-European south section of Corridor 10 in Serbia

University thought - Publication in Natural Sciences ◽

10.5937/univtho7-14957 ◽

2017 ◽

Vol 7 (2) ◽

pp. 41-46

Author(s):

Ivan Potic ◽

Milica Potic

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Environmental Stress ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Stress Detection

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Classification of Epileptic and Normal EEG Signals Using Power Spectrum of Sub-bands

Journal of Intelligent Systems with Applications ◽

10.54856/jiswa.202005095 ◽

2020 ◽

pp. 6-9

Author(s):

Sude Pehlivan ◽

Yalcin Isler

Keyword(s):

Machine Learning ◽

Early Diagnosis ◽

Power Spectrum ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Eeg Signals ◽

Power Sum ◽

Band Power ◽

Time And Energy

The early diagnosis of epilepsy, which affects the lives of many people worldwide, is the first step of treatment to help patients to continue their lives efficiently. Experts have to spend a lot of time and energy to make this diagnosis as quickly and accuratelyaspossible.The aimofthisstudywasto investigatethe capacity of machine learning algorithms to distinguish epileptic and normal signals to develop a system that can automatically diagnose seizures. LabVIEW was used to obtain the sum of EEG sub-band powers which were used as an attribute for both epileptic and normal records. These attributes were classified with different classifiers using Matlab and as a result of the classification, it was concluded that the sub-band power sum can be used as a meaningful attribute in the classification of epileptic and normal EEG signals.

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