Identification of Human Stress Based on EEG Signals Using Machine Learning

Saputra, Nophaz Hanggara; Nafi’iyah, Nur

doi:10.1109/icisit54091.2022.9872815

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2023

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Cited by 2 publications

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K-Means Clustering of the Mental Health of Engineering Students at the Universitas Islam Lamongan

Wardhani,

Nafiiyah,

Kemal Farouq

2023

2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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K-Means Clustering of the Mental Health of Engineering Students at the Universitas Islam Lamongan

Wardhani,

Nafiiyah,

Kemal Farouq

2023

2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals

Ehiabhi

Wang

2023

BioMedInformatics

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With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field.

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Identification of Human Stress Based on EEG Signals Using Machine Learning

Cited by 2 publications

References 11 publications

K-Means Clustering of the Mental Health of Engineering Students at the Universitas Islam Lamongan

K-Means Clustering of the Mental Health of Engineering Students at the Universitas Islam Lamongan

A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals

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