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 …
The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia
Z Wang, A Liu, J Yu, P Wang, Y Bi, S Xue, J Zhang… - Geroscience, 2024 - Springer
Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD)
presents a clinical challenge. Inexpensive and accessible techniques such as …
presents a clinical challenge. Inexpensive and accessible techniques such as …
[HTML][HTML] Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade …
Abstract Background and Objectives Mild Cognitive Impairment (MCI) and Alzheimer's
Disease (AD) are progressive neurological disorders that significantly impair the cognitive …
Disease (AD) are progressive neurological disorders that significantly impair the cognitive …
A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in …
Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar
symptoms, complicating their diagnosis and the development of specific treatment …
symptoms, complicating their diagnosis and the development of specific treatment …
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
Entropy measures are effective features for time series classification problems. Traditional
entropy measures, such as Shannon entropy, use probability distribution function. However …
entropy measures, such as Shannon entropy, use probability distribution function. However …
Medic: Mitigating EEG data scarcity via class-conditioned diffusion model
Learning with a small-scale Electroencephalography (EEG) dataset is a non-trivial task. On
the other hand, collecting a large-scale EEG dataset is equally challenging due to subject …
the other hand, collecting a large-scale EEG dataset is equally challenging due to subject …
N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals
The N-body problem is a remarkable research topic in physics. We propose a new feature
extraction model inspired by the N-body trajectory and test its feature extraction capability. In …
extraction model inspired by the N-body trajectory and test its feature extraction capability. In …
Detecting Alzheimer disease in EEG data with machine learning and the graph discrete fourier transform
Alzheimer Disease (AD) poses a significant and growing public health challenge worldwide.
Early and accurate diagnosis is crucial for effective intervention and care. In recent years …
Early and accurate diagnosis is crucial for effective intervention and care. In recent years …
BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories
Objective. This study aims to address the challenges associated with data-driven
electroencephalography (EEG) data analysis by introducing a standardised library called …
electroencephalography (EEG) data analysis by introducing a standardised library called …
Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity
M Sano, Y Nishiura, I Morikawa, A Hoshino, J Uemura… - PloS one, 2024 - journals.plos.org
Alpha waves, one of the major components of resting and awake cortical activity in human
electroencephalography (EEG), are known to show waxing and waning, but this …
electroencephalography (EEG), are known to show waxing and waning, but this …