Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have
potentially complementary characteristics that reflect the electrical and hemodynamic …
potentially complementary characteristics that reflect the electrical and hemodynamic …
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 …
Enhancing EEG-based mental stress state recognition using an improved hybrid feature selection algorithm
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition
require a conventional wearable device. This, in turn, requires an efficient number of EEG …
require a conventional wearable device. This, in turn, requires an efficient number of EEG …
Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application
In this study, we determine the optimal feature-combination for classification of functional
near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two …
near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two …
Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces
EA Aydin - Computer Methods and Programs in Biomedicine, 2020 - Elsevier
Abstract Background and Objective Brain-computer interfaces (BCIs) enable people to
control an external device by analyzing the brain's neural activity. Functional near-infrared …
control an external device by analyzing the brain's neural activity. Functional near-infrared …
Exploring machine learning approaches for classifying mental workload using fNIRS data from HCI tasks
Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially
more suitable (than eg EEG) for brain-based Human Computer Interaction (HCI). While …
more suitable (than eg EEG) for brain-based Human Computer Interaction (HCI). While …
Detection of mental stress through EEG signal in virtual reality environment
This paper investigates the use of an electroencephalogram (EEG) signal to classify a
subject's stress level while using virtual reality (VR). For this purpose, we designed an …
subject's stress level while using virtual reality (VR). For this purpose, we designed an …
Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings
B García-Martínez, A Martínez-Rodrigo… - Entropy, 2016 - mdpi.com
Recognition of emotions is still an unresolved challenge, which could be helpful to improve
current human-machine interfaces. Recently, nonlinear analysis of some physiological …
current human-machine interfaces. Recently, nonlinear analysis of some physiological …
Analysis of different classification techniques for two‐class functional near‐infrared spectroscopy‐based brain‐computer interface
We analyse and compare the classification accuracies of six different classifiers for a two‐
class mental task (mental arithmetic and rest) using functional near‐infrared spectroscopy …
class mental task (mental arithmetic and rest) using functional near‐infrared spectroscopy …
[PDF][PDF] Two-level classification of chronic stress using machine learning on resting-state EEG recordings
H Baumgartl, E Fezer, R Buettner - 2020 - prof-buettner.de
While there are several works that diagnose acute stress using electroencephalographic
recordings and machine learning, there are hardly any works that deal with chronic stress …
recordings and machine learning, there are hardly any works that deal with chronic stress …