AIMC Topic: Electroencephalography

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Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG.

European journal of medical research
BACKGROUND: Upper limb dysfunction after stroke seriously affects quality of life. Bilateral training has proven helpful in recovery of upper limb motor function in these patients. However, studies evaluating the effectiveness of bilateral upper limb...

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Sensors (Basel, Switzerland)
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor functio...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Journal of integrative bioinformatics
Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has ...

Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training a robust deep learning network. Transfer learning and their variations are effective tools in impro...

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.

NeuroImage
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decad...

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization...

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Neural networks : the official journal of the International Neural Network Society
Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined wit...

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Sensors (Basel, Switzerland)
Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an...

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.

Artificial intelligence in medicine
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-ba...