AIMC Topic: Electroencephalography

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Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder.

Journal of affective disorders
BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-scale neural networks. Methamphetamine use disorder (MUD) and obsessive-compulsive disorder (OCD) exh...

Detecting mind wandering via EEG and facial video features.

Behavior research methods
PURPOSE: Mind wandering (MW), a common cognitive phenomenon marked by a shift of attention away from the task at hand, poses significant challenges in online educational settings. This study aims to advance MW detection by developing a classification...

Novel electroencephalographic biomarkers for the prediction of responders to an experimental glutamatergic agent in patients with schizophrenia.

Translational psychiatry
All medications currently used to treat schizophrenia, which exert their therapeutic effects by inhibiting dopaminergic neurotransmission, have their greatest efficacy against the positive symptoms of schizophrenia but have limited impact on negative...

Source-free domain adaptation for SSVEP-based brain-computer interfaces.

Journal of neural engineering
Steady-state visually evoked potential-based Brain-computer interface (BCI) spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most...

EEG workload estimation and classification: a systematic review.

Journal of neural engineering
Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. Machine Learning (ML) and deep learning (DL) techniques have been increasingly employed to develop accurate workload estim...

Understanding the relationship between rosemary odor and mental workload through deep learning.

Neuroscience
This research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participat...

ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.

Scientific reports
Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive...

A machine learning approach for detection of claustrophobic brain activity in electroencephalography.

Scientific reports
Claustrophobia, a phobia with a specific unreasonable and excessive fear of enclosed spaces, can have a considerable impact on an individual's life. Electroencephalography (EEG) has been a tool with potential for studying neural processes in anxiety ...

Error-related potentials in EEG signals: feature-based detection for human-robot interaction.

Scientific reports
This study explores how to improve the detection of Error-Related Potentials (ErrPs), namely brain signals generated when a person perceives an unexpected action performed by an interacting agent. ErrPs are promising for improving interactions betwee...

Maturation of Neuronal Activity in the Human Cortex Exhibits Robust Spatial Gradients across the Birth Transition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Early structural and molecular development of the human cortex is extensively studied, but little is known about the development of neuronal activity across cortical regions. We used dense array electroencephalography recordings and a machine learnin...