Discrimination of Real and Deep Fake Videos using EEG Signals.

Deepfake technology can create highly realistic fabricated videos, presenting serious ethical concerns and threats of misinformation. Reliably distinguishing deepfakes from genuine videos is therefore critical yet challenging. This study explored electroencephalography (EEG)-based deepfake detection by analyzing EEG responses in 10 participants viewing 100 videos (50 real, 50 deepfakes).

Detecting Deepfakes with Super-Resolution EEG.

Electroencephalogram (EEG) signals play a crucial role as biomarkers of brain activity, providing valuable insights into neural processes. The spatial resolution of EEG, determined by the number of channels, is essential for obtaining a comprehensive understanding of brain functions. However, low-resolution EEG systems pose a significant challenge in capturing detailed brain processes.

Assessment of Brain Function After 240 Days Confinement Using Functional Near Infrared Spectroscopy.

Future space exploration missions will expose astronauts to various stressors, making the early detection of mental stress crucial for prolonged missions. Our study proposes using functional near infrared spectroscopy (fNIRS) combined with multiple machine learning models to assess the level of mental stress. The objective is to identify and quantify stress levels during 240 days confinement scenario.

Obstacle Avoidance in Healthy Adults and People With Multiple Sclerosis: Preliminary fNIRS Study.

This study examined how gait adaptation during predictable and non-predictable obstacle avoidance affects the sensorimotor network in both healthy controls (HC) and persons with multiple sclerosis (pwMS). We utilized fNIRS measurements of HbO2 and HHb to estimate cortical activations and connectivity networks, which were then analyzed using power spectral density (PSD) and partial directed coherence (PDC). The findings revealed distinct patterns of cortical activation and connectivity for each task condition in both groups.

One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility.

Mental Stress and Cognitive Deficits Management.

Mental stress is a prevalent aspect of contemporary life that affects individuals from diverse backgrounds [...

Mental Stress Detection and Mitigation using Machine Learning and Binaural Beat Stimulation.

Stress is an inevitable problem experienced by people worldwide. Continuous exposure to stress can greatly impact mental activity as well as physical health thereby leading to several diseases. In this study, we investigate the effectiveness of audio binaural beat stimulation (BBs) in mitigating mental stress.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolutional neural networks (CNNs) to classify four mental states: rest, control-alert, stress and stress mitigation. The mental stress state was induced using Stroop color word test (SCWT) with time constrains and was then mitigated using 16 Hz Binaural beat stimulation (BBs).

Detection of Astronaut's Stress Levels During 240-Day Confinement using EEG Signals and Machine Learning.

Early detection of mental stress is particularly important in prolonged space missions. In this study, we propose utilizing electroencephalography (EEG) with multiple machine learning models to detect elevated stress levels during a 240-day confinement. We quantified the levels of stress using alpha amylase levels, reaction time (RT) to stimuli, accuracy of target detection, and functional connectivity of EEG estimated by Phase Locking Value (PLV).

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications.

Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

[This corrects the article DOI: 10.3389/fpsyt.2023.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Introduction: The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks.

Methods: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta).

Mental Stress Management Using fNIRS Directed Connectivity and Audio Stimulation.

In this study, we propose a method to enhance cognitive vigilance and mitigate mental stress in the workplace. We designed an experiment to induce stress by putting participants through Stroop Color-Word Task (SCWT) under time constraint and negative feedback. Then, we used 16 Hz binaural beats auditory stimulation (BBs) for 10 minutes to enhance cognitive vigilance and mitigate stress.

Stress management using fNIRS and binaural beats stimulation.

In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs.

Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.

Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance.

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 channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance.

Asymmetry of Regional Phase Synchrony Cortical Networks Under Cognitive Alertness and Vigilance Decrement States.

This study investigates intra-regional connectivity and regional hemispheric asymmetry under two vigilance states: alertness and vigilance decrement. The vigilance states were induced on nine healthy subjects while performing 30 min in-congruent Stroop color-word task (I-SCWT). We measured brain activity using Electroencephalography (EEG) signals with 64-channels.

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features.

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 stress at its early stages, i.e.

A Review on Mental Stress Assessment Methods Using EEG Signals.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace.

Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States.

In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT.

Vigilance Decrement and Enhancement Techniques: A Review.

This paper presents the first comprehensive review on vigilance enhancement using both conventional and unconventional means, and further discusses the resulting contradictory findings. It highlights the key differences observed between the research findings and argues that variations of the experimental protocol could be a significant contributing factor towards such contradictory results. Furthermore, the paper reveals the effectiveness of unconventional means of enhancement in significant reduction of vigilance decrement compared to conventional means.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study.

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions.

Mental stress grading based on fNIRS signals.

In this study, we propose functional near infrared spectroscopy (fNIRS) to objectively grade different levels of mental stress. The levels of stress were set based on the difficulty of arithmetic task, time pressure and negative feedback about peer performance. We examined the proposed approach on twelve human subjects using the Montreal Imaging Stress Task.