Cognitive workload recognition using EEG signals and machine learning: A review
Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …
development of operator mental state monitoring, especially for cognitive workload …
Recent approaches on classification and feature extraction of EEG signal: A review
Objective: Electroencephalography (EEG) has an influential role in neuroscience and
commercial applications. Most of the tools available for EEG signal analysis use machine …
commercial applications. Most of the tools available for EEG signal analysis use machine …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis
LPA Arts, EL van den Broek - Nature Computational Science, 2022 - nature.com
The spectral analysis of signals is currently either dominated by the speed–accuracy trade-
off or ignores a signal's often non-stationary character. Here we introduce an open-source …
off or ignores a signal's often non-stationary character. Here we introduce an open-source …
Evolutionary inspired approach for mental stress detection using EEG signal
Stress is a pensive issue in our competitive world and it has a huge impact on physical and
mental health. Severe health issues may arise due to long exposure of stress. Hence, its …
mental health. Severe health issues may arise due to long exposure of stress. Hence, its …
An effective mental stress state detection and evaluation system using minimum number of frontal brain electrodes
O Attallah - Diagnostics, 2020 - mdpi.com
Currently, mental stress is a common social problem affecting people. Stress reduces
human functionality during routine work and may lead to severe health defects. Detecting …
human functionality during routine work and may lead to severe health defects. Detecting …
Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection
J Yedukondalu, LD Sharma - Biomedical Signal Processing and Control, 2023 - Elsevier
Cognitive load detection during the mental assignment of neural activity is necessary
because it helps to understand the brain's response to stimuli. An electroencephalogram …
because it helps to understand the brain's response to stimuli. An electroencephalogram …
Automated mental arithmetic performance detection using quantum pattern-and triangle pooling techniques with EEG signals
Background Electroencephalography (EEG) signals recorded during mental arithmetic tasks
can be used to quantify mental performance. The classification of these input EEG signals …
can be used to quantify mental performance. The classification of these input EEG signals …
Advanced bioelectrical signal processing methods: Past, present and future approach—Part II: Brain signals
As it was mentioned in the previous part of this work (Part I)—the advanced signal
processing methods are one of the quickest and the most dynamically developing scientific …
processing methods are one of the quickest and the most dynamically developing scientific …
EEG microstate features according to performance on a mental arithmetic task
In this study, we hypothesized that task performance could be evaluated applying EEG
microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of …
microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of …