ArticleFrontiers in aging neuroscience2014
The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.
Article in Frontiers in aging neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.Disease markers · 2018Pooled it
- PhysioMotion Artifact: A task-driven EEG dataset with point-wise motion artifact annotations.Scientific data · 2026Article
- Automatic detection of Alzheimer's disease from EEG signals using an improved AFS-GA hybrid algorithm.Cognitive neurodynamics · 2024Article
- Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis.Computational intelligence and neuroscience · 2023Article
- Evaluating the Alterations Induced by Virtual Reality in Cerebral Small-World Networks Using Graph Theory Analysis with Electroencephalography.Brain sciences · 2022Article
- Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note.Sensors (Basel, Switzerland) · 2022Review
- Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network.Frontiers in aging neuroscience · 2022Article
- Multimodal resting-state connectivity predicts affective neurofeedback performance.Frontiers in human neuroscience · 2022Article
- Review
- Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI.Frontiers in human neuroscience · 2021Article
- Adaptive Filtering for Improved EEG-Based Mental Workload Assessment of Ambulant Users.Frontiers in neuroscience · 2021Article
- A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.Healthcare (Basel, Switzerland) · 2020Article
- Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia.Journal of personalized medicine · 2020Review
- PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research.Frontiers in neuroscience · 2020Article
- Article
- On-line EEG Denoising and Cleaning Using Correlated Sparse Recovery and Active Learning.International journal of wireless information networks · 2017Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.