Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
EEG signal processing and supervised machine learning to early diagnose Alzheimer's disease
D Pirrone, E Weitschek, P Di Paolo, S De Salvo… - Applied sciences, 2022 - mdpi.com
Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
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 deep learning based framework for diagnosis of mild cognitive impairment
Detecting mild cognitive impairment (MCI) from electroencephalography (EEG) data is a
challenging problem as existing methods rely on machine learning based shallow …
challenging problem as existing methods rely on machine learning based shallow …
Automatic and efficient framework for identifying multiple neurological disorders from EEG signals
The burden of neurological disorders is huge on global health and recognized as major
causes of death and disability worldwide. There are more than 600 neurological diseases …
causes of death and disability worldwide. There are more than 600 neurological diseases …
EEG based classification of children with learning disabilities using shallow and deep neural network
NPG Seshadri, S Agrawal, BK Singh… - … Signal Processing and …, 2023 - Elsevier
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 …
Automatic detection of Alzheimer's disease from EEG signals using low-complexity orthogonal wavelet filter banks
Background: Alzheimer's disease (AD) is one of the most common neurodegenerative
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …
A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders in the
world. Although there is no known cure for it at the present, preventive drug trials and …
world. Although there is no known cure for it at the present, preventive drug trials and …