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 …
Brain-computer interfaces systems for upper and lower limb rehabilitation: a systematic review
D Camargo-Vargas, M Callejas-Cuervo, S Mazzoleni - Sensors, 2021 - mdpi.com
In recent years, various studies have demonstrated the potential of electroencephalographic
(EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of …
(EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
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) …
[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 …
Local pattern transformation based feature extraction for recognition of Parkinson's disease based on gait signals
Parkinson's disease (PD) is a neuro-degenerative disorder primarily triggered due to the
deterioration of dopamine-producing neurons in the substantia nigra of the human brain …
deterioration of dopamine-producing neurons in the substantia nigra of the human brain …
Classification of EEG signals from young adults with dyslexia combining a Brain Computer Interface device and an Interactive Linguistic Software Tool
The magnocellular pathway deficit theory has long been considered to be a possible cause
for dyslexia, providing an alternative method to explain auditory and visual processing …
for dyslexia, providing an alternative method to explain auditory and visual processing …
[HTML][HTML] A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG
E Perez-Valero, MÁ Lopez-Gordo, CM Gutiérrez… - Computer Methods and …, 2022 - Elsevier
Early detection is critical to control Alzheimer's disease (AD) progression and postpone
cognitive decline. Traditional medical procedures such as magnetic resonance imaging are …
cognitive decline. Traditional medical procedures such as magnetic resonance imaging are …