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 …
Data mining algorithms and techniques in mental health: a systematic review
Data Mining in medicine is an emerging field of great importance to provide a prognosis and
deeper understanding of disease classification, specifically in Mental Health areas. The …
deeper understanding of disease classification, specifically in Mental Health areas. The …
Combining EEG signal processing with supervised methods for Alzheimer's patients classification
Abstract Background Alzheimer's Disease (AD) is a neurodegenaritive disorder
characterized by a progressive dementia, for which actually no cure is known. An early …
characterized by a progressive dementia, for which actually no cure is known. An early …
Eeg-based alzheimer's disease recognition using robust-pca and lstm recurrent neural network
M Alessandrini, G Biagetti, P Crippa, L Falaschetti… - Sensors, 2022 - mdpi.com
The use of electroencephalography (EEG) has recently grown as a means to diagnose
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
A dementia classification framework using frequency and time-frequency features based on EEG signals
Alzheimer's disease (AD) accounts for 60%–70% of all dementia cases, and clinical
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for …
PM Rossini, F Miraglia, F Vecchio - Alzheimer's & Dementia, 2022 - Wiley Online Library
Introduction Dementia in its various forms represents one of the most frightening
emergencies for the aging population. Cognitive decline—including Alzheimer's disease …
emergencies for the aging population. Cognitive decline—including Alzheimer's disease …
The feature, performance, and prospect of advanced electrodes for electroencephalogram
Recently, advanced electrodes have been developed, such as semi-dry, dry contact, dry non-
contact, and microneedle array electrodes. They can overcome the issues of wet electrodes …
contact, and microneedle array electrodes. They can overcome the issues of wet electrodes …
Machine learning and regression analysis to model the length of hospital stay in patients with femur fracture
Fractures of the femur are a frequent problem in elderly people, and it has been
demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h …
demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h …
Neurological abnormality detection from electroencephalography data: a review
The efficient detection of neurological abnormalities (disorders) is very important in clinical
diagnosis for modern medical applications. As stated by the World Health Organization's …
diagnosis for modern medical applications. As stated by the World Health Organization's …
A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals
W Xia, R Zhang, X Zhang, M Usman - Heliyon, 2023 - cell.com
Abstract Background The diagnosis of Alzheimer's disease (AD) using
electroencephalography (EEG) has garnered more attention recently. New methods In this …
electroencephalography (EEG) has garnered more attention recently. New methods In this …