A review of EEG signal features and their application in driver drowsiness detection systems
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …
is often approached using neurophysiological signals as the basis for building a reliable …
Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review
Recent developments and studies in brain-computer interface (BCI) technologies have
facilitated emotion detection and classification. Many BCI studies have sought to investigate …
facilitated emotion detection and classification. Many BCI studies have sought to investigate …
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 …
Interpretable emotion recognition using EEG signals
Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests
in recent years and has been broadly adopted in medical, affective computing, and other …
in recent years and has been broadly adopted in medical, affective computing, and other …
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 …
EEG-based emotion analysis using non-linear features and ensemble learning approaches
Recognition of emotions based on electroencephalography (EEG) has become one of the
most emerging topics for healthcare, education system, knowledge sharing, gaming, and …
most emerging topics for healthcare, education system, knowledge sharing, gaming, and …
A review on nonlinear methods using electroencephalographic recordings for emotion recognition
B García-Martínez, A Martinez-Rodrigo… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Electroencephalographic (EEG) recordings are receiving growing attention in the field of
emotion recognition, since they monitor the brain's first response to an external stimulus …
emotion recognition, since they monitor the brain's first response to an external stimulus …
Machine learning classification of maladaptive rumination and cognitive distraction in terms of frequency specific complexity
In this study, cognitive and behavioral emotion regulation strategies (ERS) are classified by
using machine learning models driven by a new local EEG complexity approach so called …
using machine learning models driven by a new local EEG complexity approach so called …
Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals
Emotion plays an important role in mental and physical health, decision-making, and social
communication. An accurate detection of human emotions is critical to ensure effective …
communication. An accurate detection of human emotions is critical to ensure effective …
Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor
Cognitive load recognition during mental arithmetic activity facilitates to observe and identify
the brain's response towards stress stimulus. As a result, an efficient mental load …
the brain's response towards stress stimulus. As a result, an efficient mental load …