Machine learning algorithms and statistical approaches for Alzheimer's disease analysis based on resting-state EEG recordings: A systematic review
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of
dementia with a great prevalence in western countries. The diagnosis of AD and its …
dementia with a great prevalence in western countries. The diagnosis of AD and its …
Analysis of features of Alzheimer's disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in
which there are small variants of brain changes among the intermediate stages. Although …
which there are small variants of brain changes among the intermediate stages. Although …
Neuro-nutraceutical potential of Asparagus racemosus: A review
Debilitating neuropsychiatric and neurodegenerative conditions are associated with
complex multifactorial pathophysiology. Their treatment strategies often only provide …
complex multifactorial pathophysiology. Their treatment strategies often only provide …
[HTML][HTML] Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection
Stress has become a dangerous health problem in our life, especially in student education
journey. Accordingly, previous methods have been conducted to detect mental stress based …
journey. Accordingly, previous methods have been conducted to detect mental stress based …
Alzheimer's disease and frontotemporal dementia: A robust classification method of EEG signals and a comparison of validation methods
Dementia is the clinical syndrome characterized by progressive loss of cognitive and
emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's …
emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's …
DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals
Objective: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection
Background and objective Alzheimer's disease (AD) is characterized by cognitive,
behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to …
behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to …
Complexity of EEG dynamics for early diagnosis of Alzheimer's disease using permutation entropy neuromarker
Background and objective Electroencephalogram (EEG) is one of the most demanded
screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain …
screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain …
EEG based classification of children with learning disabilities using shallow and deep neural network
Learning disability (LD), a neurodevelopmental disorder that has severely impacted the lives
of many children all over the world. LD refers to significant deficiency in children's reading …
of many children all over the world. LD refers to significant deficiency in children's reading …
EEG-based eye movement recognition using brain–computer interface and random forests
Discrimination of eye movements and visual states is a flourishing field of research and
there is an urgent need for non-manual EEG-based wheelchair control and navigation …
there is an urgent need for non-manual EEG-based wheelchair control and navigation …