[HTML][HTML] Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

O AlShorman, M Masadeh, MBB Heyat… - Journal of integrative …, 2022 - imrpress.com
Stress has become a dangerous health problem in our life, especially in student education
journey. Accordingly, previous methods have been conducted to detect mental stress based …

Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework

RJ Deligani, SB Borgheai, J McLinden… - Biomedical optics …, 2021 - opg.optica.org
Multimodal data fusion is one of the current primary neuroimaging research directions to
overcome the fundamental limitations of individual modalities by exploiting complementary …

Fusing near-infrared spectroscopy with wearable hemodynamic measurements improves classification of mental stress

NZ Gurel, H Jung, S Hersek, OT Inan - IEEE Sensors Journal, 2018 - ieeexplore.ieee.org
Human-computer interaction technology, and the automatic classification of a person's
mental state, are of interest to multiple industries. In this paper, the fusion of sensing …

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate

P Mukherjee, A Halder Roy - Computer Methods in Biomechanics …, 2023 - Taylor & Francis
In today's world, people suffer from many fatal maladies, and stress is one of them.
Excessive stress can have deleterious effects on the health, brain, mind, and nervous …

[HTML][HTML] Hybrid deep learning approach for stress detection using decomposed eeg signals

B Roy, L Malviya, R Kumar, S Mal, A Kumar… - Diagnostics, 2023 - mdpi.com
Stress has an impact, not only on a person's physical health, but also on the ability to
perform at the workplace in daily life. The well-established relation between psychological …

EEG-based detection of cognitive load using VMD and LightGBM classifier

P Jain, J Yedukondalu, H Chhabra, U Chauhan… - International Journal of …, 2024 - Springer
Cognitive load, which alters neuronal activity, is essential to understanding how the brain
reacts to stress. This work aims to classify electroencephalogram (EEG) signals to detect …

[HTML][HTML] Measuring mental workload with EEG+ fNIRS

H Aghajani, M Garbey, A Omurtag - Frontiers in human neuroscience, 2017 - frontiersin.org
We studied the capability of a Hybrid functional neuroimaging technique to quantify human
mental workload (MWL). We have used electroencephalography (EEG) and functional near …

Machine learning framework for the detection of mental stress at multiple levels

AR Subhani, W Mumtaz, MNBM Saad, N Kamel… - IEEE …, 2017 - ieeexplore.ieee.org
Mental stress has become a social issue and could become a cause of functional disability
during routine work. In addition, chronic stress could implicate several psychophysiological …

[HTML][HTML] EEG mental stress assessment using hybrid multi-domain feature sets of functional connectivity network and time-frequency features

A Hag, D Handayani, T Pillai, T Mantoro, MH Kit… - Sensors, 2021 - mdpi.com
Exposure to mental stress for long period leads to serious accidents and health problems.
To avoid negative consequences on health and safety, it is very important to detect mental …

[HTML][HTML] fNIRS evaluation of frontal and temporal cortex activation by verbal fluency task and high-level cognition task for detecting anxiety and depression

X Lang, D Wen, Q Li, Q Yin, M Wang, Y Xu - Frontiers in Psychiatry, 2021 - frontiersin.org
Anxiety and depression are widespread psychosis which are believed to affect cerebral
metabolism, especially in frontal and temporal cortex. The comorbidity patients of anxiety …