A review on mental stress detection using wearable sensors and machine learning techniques
Stress is an escalated psycho-physiological state of the human body emerging in response
to a challenging event or a demanding condition. Environmental factors that trigger stress …
to a challenging event or a demanding condition. Environmental factors that trigger stress …
Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis
LE Ismail, W Karwowski - Plos one, 2020 - journals.plos.org
Background Neuroergonomics combines neuroscience with ergonomics to study human
performance using recorded brain signals. Such neural signatures of performance can be …
performance using recorded brain signals. Such neural signatures of performance can be …
[HTML][HTML] Towards objective human performance measurement for maritime safety: A new psychophysiological data-driven machine learning method
Human errors significantly contribute to transport accidents. Human performance
measurement (HPM) is crucial to ensure human reliability and reduce human errors …
measurement (HPM) is crucial to ensure human reliability and reduce human errors …
Measuring workers' emotional state during construction tasks using wearable EEG
Construction workers' emotional states (eg, pleasure, displeasure, excitement, and
relaxation) are known as a critical factor that affect their performance (eg, safety, health, and …
relaxation) are known as a critical factor that affect their performance (eg, safety, health, and …
[HTML][HTML] Incorporation of seafarer psychological factors into maritime safety assessment
Psychological factors have been a critical cause of human errors in sectors such as health
and aviation. However, there is little relevant research in the maritime industry, even though …
and aviation. However, there is little relevant research in the maritime industry, even though …
Multi-method fusion of cross-subject emotion recognition based on high-dimensional EEG features
F Yang, X Zhao, W Jiang, P Gao, G Liu - Frontiers in computational …, 2019 - frontiersin.org
Emotion recognition using electroencephalogram (EEG) signals has attracted significant
research attention. However, it is difficult to improve the emotional recognition effect across …
research attention. However, it is difficult to improve the emotional recognition effect across …
Past, present and future of research on wearable technologies for healthcare: a bibliometric analysis using Scopus
YM de-la-Fuente-Robles, AJ Ricoy-Cano… - Sensors, 2022 - mdpi.com
Currently, wearable technology is present in different fields that aim to satisfy our needs in
daily life, including the improvement of our health in general, the monitoring of patient …
daily life, including the improvement of our health in general, the monitoring of patient …
Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders
S Yang, Z Yin, Y Wang, W Zhang, Y Wang… - Computers in biology and …, 2019 - Elsevier
To estimate the reliability and cognitive states of operator performance in a human-machine
collaborative environment, we propose a novel human mental workload (MW) recognizer …
collaborative environment, we propose a novel human mental workload (MW) recognizer …
Online learning for wearable EEG-based emotion classification
S Moontaha, FEF Schumann, B Arnrich - Sensors, 2023 - mdpi.com
Giving emotional intelligence to machines can facilitate the early detection and prediction of
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …
[HTML][HTML] SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image …
This paper presents a collection of electroencephalogram (EEG) data recorded from 40
subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the …
subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the …