AIMC Topic: Electroencephalography

Clear Filters Showing 1951 to 1960 of 2295 articles

Event-related potentials reveal incongruent behavior of autonomous vehicles in the moral machine dilemma.

Scientific reports
We investigated event-related potentials (ERPs) in the context of autonomous vehicles (AVs)-specifically in ambiguous, morally challenging traffic situations. In our study, participants (n = 34) observed a putative artificial intelligence (AI) making...

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Schizophrenia bulletin
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...

An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data.

Scientific reports
Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cognitive fatigue is a critical condition that can significantly impair attention and performance, among ...

Mental state classification based on electroencephalogram (EEG) using multiclass support vector machine.

The Medical journal of Malaysia
INTRODUCTION: Mental state refers to a person's state of mind from various perspectives, including consciousness, intention, and functionalism. Mental states closely related to everyday life include the concentrating state, neutral state, and relaxat...

Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG.

Human brain mapping
Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to obse...

An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.

Human factors
ObjectiveWe aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.BackgroundTraditional methods of evaluating mental ...

Neural dynamics of mental state attribution to social robot faces.

Social cognitive and affective neuroscience
The interplay of mind attribution and emotional responses is considered crucial in shaping human trust and acceptance of social robots. Understanding this interplay can help us create the right conditions for successful human-robot social interaction...

Machine Learning and Deep Learning in Detection of Neonatal Seizures: A Systematic Review.

Journal of evaluation in clinical practice
BACKGROUND: Neonatal seizures are one of the most prevalent clinical manifestations of neurological conditions, requiring urgent intervention and detection. Machine learning (ML) and Deep Learning (DL) is an emerging promising tool for detecting and ...

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.

Neural computation
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation....

Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

International journal of neural systems
Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD...