AIMC Topic: Electroencephalography

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Multimodal autism detection: Deep hybrid model with improved feature level fusion.

Computer methods and programs in biomedicine
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...

Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.

Scientific data
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and ...

Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.

Sensors (Basel, Switzerland)
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contr...

Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.

Journal of neurology
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
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
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
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
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
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)
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 ...