AIMC Topic: Electroencephalography
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Enhanced Graph Attention Network by Integrating Transformer for Epileptic EEG Identification.
International journal of neural systems
Aug 1, 2025
Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing t...
Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.
IEEE transactions on bio-medical engineering
Aug 1, 2025
OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communica...
Decoding the Narcissistic Brain.
NeuroImage
Jul 15, 2025
There is a substantial knowledge gap in the narcissism literature: <1 % of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spon...
EEG-based prediction of reaction time during sleep deprivation.
Sleep
Jul 11, 2025
Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics of neuronal networks in the brain and can even lead to hallucinations and epileptic seizures. In cogn...
Deep learning on high-density EEG during a cognitive task distinguishes patients with Parkinson's disease from healthy controls.
Journal of neural engineering
Jul 3, 2025
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis, which used to be based on clinical assessment, increasingly relies on biomarkers. While electroence...
Annotating neurophysiologic data at scale with optimized human input.
Journal of neural engineering
Jul 3, 2025
Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to...
A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.
Medical engineering & physics
Jul 1, 2025
Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats...
Augmenting Common Spatial Patterns to deep learning networks for improved alcoholism detection using EEG signals.
Computers in biology and medicine
Jul 1, 2025
One of the main risk factors for numerous health problems is excessive drinking. Alcoholism is a severe disorder that can affect a person's thinking and cognitive abilities. Early detection of alcoholism can help the subject regain control over their...
Application of EEG microstates in Parkinson's disease.
Parkinsonism & related disorders
Jul 1, 2025
Electroencephalography (EEG) microstate analysis is a promising technique for detecting transient brain dynamics and identifying disease-specific biomarkers in Parkinson's disease (PD). By capturing subsecond fluctuations in brain activity with intri...
Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.
Neuroscience and biobehavioral reviews
Jul 1, 2025
Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contrast, machine learning models have the potential to classify and diagnose MDD more effectively, reduc...
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