AIMC Topic: Electroencephalography

Clear Filters Showing 201 to 210 of 2115 articles

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Brain research bulletin Jan 15, 2025
Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste ...
Memory, Short-Term Humans Cognition Neural Networks, Computer Models, Neurological Brain Electroencephalography
View on PubMed DOI

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Scientific reports Jan 15, 2025
Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in ...
Aged tau Proteins Amyloid beta-Peptides Alzheimer Disease Machine Learning Middle Aged Humans Biomarkers Phosphorylation Female Electroencephalography Aged, 80 and over Male
View on PubMed DOI

Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding.

IEEE transactions on bio-medical engineering Jan 15, 2025
Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-free for cross-subject applications remains challenging due to domain shift problem inherent in such...
Electroencephalography Algorithms Humans Signal Processing, Computer-Assisted Adult Supervised Machine Learning Male
View on PubMed DOI

A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.

Sensors (Basel, Switzerland) Jan 13, 2025
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces...
Brain-Computer Interfaces Brain Convolutional Neural Networks Imagination Humans Electroencephalography Neural Networks, Computer
View on PubMed DOI

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Sensors (Basel, Switzerland) Jan 12, 2025
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) h...
Depressive Disorder, Major Depression Humans Electroencephalography Machine Learning Male Female Adult Middle Aged
View on PubMed DOI

A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2025
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification...
Brain Deep Learning Emotions Humans Neural Networks, Computer Electroencephalography Functional Laterality
View on PubMed DOI

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Journal of Alzheimer's disease : JAD Jan 8, 2025
BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effec...
Electroencephalography Entropy Middle Aged Cognitive Dysfunction Principal Component Analysis Humans Machine Learning Aged Brain Aged, 80 and over Female Male
View on PubMed DOI

A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.

Scientific reports Jan 8, 2025
The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional ...
Neural Networks, Computer Adult Male Humans Brain-Computer Interfaces Movement Deep Learning Electroencephalography
View on PubMed DOI

Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Neural networks : the official journal of the International Neural Network Society Jan 7, 2025
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. Th...
Brain Deep Learning Electroencephalography Attention Brain-Computer Interfaces Evoked Potentials Humans Neural Networks, Computer Algorithms
View on PubMed DOI

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Journal of psychiatric research Jan 7, 2025
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...
Nerve Net Obsessive-Compulsive Disorder Female Humans Young Adult Male Machine Learning Adult Middle Aged Electroencephalography
View on PubMed DOI
Popular Topics
Recent Journals