AIMC Topic: Electroencephalography
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Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.
Journal of neurology
Dec 16, 2024
BACKGROUND: Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a surv...
Identification of autism spectrum disorder using electroencephalography and machine learning: a review.
Journal of neural engineering
Dec 16, 2024
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by communication barriers, societal disengagement, and monotonous actions. Traditional diagnostic methods for ASD rely on clinical observations and behavioural assessments...
Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.
Journal of neuroscience methods
Dec 14, 2024
BACKGROUND: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for in...
How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.
Epilepsy & behavior : E&B
Dec 13, 2024
IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may delay the initiation of the ASM regimen that might optimally balance efficacy and tolerability for in...
Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Dec 12, 2024
OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain functional connectivity (FC), affecting over 100 million people worldwide. This study aims to identify disti...
Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.
Computers in biology and medicine
Dec 12, 2024
This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currentl...
Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.
Sensors (Basel, Switzerland)
Dec 12, 2024
Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked ...
Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.
Sensors (Basel, Switzerland)
Dec 11, 2024
With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest wit...
Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Dec 11, 2024
BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological ...
Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model.
Biomedical physics & engineering express
Dec 11, 2024
The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can...
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