AIMC Topic: Electroencephalography

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Defining individualized theta frequency for memory modulation: A machine learning approach across brain states and regions.

NeuroImage
Recent transcranial alternating current stimulation (tACS) studies suggest that theta-frequency stimulation can modulate memory performance, with evidence highlighting individual variability in optimal stimulation frequency. However, it remains uncle...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.

Experimental brain research
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color diff...

A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.

BMC psychiatry
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition affecting approximately 3.5% of the global population, with diagnosis on average delayed by 7.1 years or often confounded with other psychiatric disorders. Advances in electroen...

EEG Connectivity is an Objective Signature of Reduced Consciousness and Sleep Depth.

Brain topography
Different levels of reduced consciousness characterise human sleep stages at the behavioural level. On electroencephalography (EEG), the identification of sleep stages predominantly relies on localised oscillatory power within distinct frequency band...

Improving attachment style clustering with ROCKET and CatBoost: Insights from EEG analysis.

PloS one
Understanding attachment styles is essential in psychology and neuroscience, yet predicting them using objective neural data remains challenging. This study explores the use of machine learning (ML) models and EEG analysis to improve attachment style...

Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM.

Scientific reports
The analysis of EEG signals for determining emotion is one of the most important topics in the field of artificial intelligence. It can be applied in a wide variety of areas, such as emotional health care and the man/machine interface. The purpose of...

A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.

Scientific reports
In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases ...

The impact of prompting on ChatGPT's adherence to status epilepticus treatment guidelines.

Scientific reports
This study assessed ChatGPT's adherence to established management guidelines for status epilepticus (SE) from major neurological societies (NCS, AES, EFNS) and examined how prompt specificity affected the quality of its recommendations. Four prompts ...

ROC Analysis of Biomarker Combinations in Fragile X Syndrome-Specific Clinical Trials: Evaluating Treatment Efficacy via Exploratory Biomarkers.

Translational psychiatry
Fragile X Syndrome (FXS) is a rare neurodevelopmental disorder caused by a trinucleotide repeat expansion on the 5' untranslated region of the FMR1 gene. FXS is characterized by intellectual disability, anxiety, sensory hypersensitivity, and difficul...