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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment
Duyan Geng, Chao Wang, Zhigang Fu, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 20

Showing 20 citing articles:

STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network
Pramod Kachare, Sandeep B. Sangle, Digambar Puri, et al.
Cognitive Neurodynamics (2024) Vol. 18, Iss. 5, pp. 3195-3208
Closed Access | Times Cited: 13
LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection
Pramod Kachare, Digambar Puri, Sandeep B. Sangle, et al.
Physical and Engineering Sciences in Medicine (2024)
Closed Access | Times Cited: 9
Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers
Jaeyoon Song, E. Cho, H. -J. Lee, et al.
Biosensors (2025) Vol. 15, Iss. 2, pp. 102-102
Open Access | Times Cited: 1
Prediction of dementia based on older adults’ sleep disturbances using machine learning
Joel Nyholm, Ahmad Nauman Ghazi, Sarah Nauman Ghazi, et al.
Computers in Biology and Medicine (2024) Vol. 171, pp. 108126-108126
Open Access | Times Cited: 8
Mild Cognitive Impairment detection based on EEG and HRV data
Amal Boudaya, Siwar Chaabene, Bassem Bouaziz, et al.
Digital Signal Processing (2024) Vol. 147, pp. 104399-104399
Open Access | Times Cited: 7
Metaheuristic optimized time–frequency features for enhancing Alzheimer’s disease identification
Digambar Puri, Pramod Kachare, Sanjay L. Nalbalwar
Biomedical Signal Processing and Control (2024) Vol. 94, pp. 106244-106244
Closed Access | Times Cited: 7
Consensus on rapid screening for prodromal Alzheimer’s disease in China
Lin Huang, Qinjie Li, Yao Lu, et al.
General Psychiatry (2024) Vol. 37, Iss. 1, pp. e101310-e101310
Open Access | Times Cited: 6
Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach
Anna Michela Gaeta, María Quijada-López, Ferrán Barbé, et al.
Frontiers in Aging Neuroscience (2024) Vol. 16
Open Access | Times Cited: 6
STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis
Hao Zhou, Liyong Yin, Rui Su, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 248, pp. 108123-108123
Closed Access | Times Cited: 5
Optimal time-frequency localized wavelet filters for identification of Alzheimer’s disease from EEG signals
Digambar Puri, Jayanand P. Gawande, Pramod Kachare, et al.
Cognitive Neurodynamics (2025) Vol. 19, Iss. 1
Closed Access
Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection
Digambar Puri, Pramod Kachare, Smith K. Khare, et al.
Journal of Bionic Engineering (2025)
Closed Access
Machine and Deep Learning Trends in EEG-Based Detection and Diagnosis of Alzheimer’s Disease: A Systematic Review
Marcos Avilés, Luz-María Sánchez, José M. Álvarez-Alvarado, et al.
Eng—Advances in Engineering (2024) Vol. 5, Iss. 3, pp. 1464-1484
Open Access | Times Cited: 3
Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices
Ruixuan Wu, Aoyu Li, Chen Xue, et al.
Brain Sciences (2023) Vol. 13, Iss. 8, pp. 1222-1222
Open Access | Times Cited: 7
Prediction of mild cognitive impairment using EEG signal and BiLSTM network
Tahani Jaser Alahmadi, Atta Ur Rahman, Zaid Ali Alhababi, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 2, pp. 025028-025028
Open Access | Times Cited: 2
Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals
Afrah Said, Hanife Göker
Cognitive Neurodynamics (2023) Vol. 18, Iss. 2, pp. 597-614
Closed Access | Times Cited: 4
DAEEGViT: A domain adaptive vision transformer framework for EEG cognitive state identification
Yu Ouyang, Yang Liu, Liang Shan, et al.
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 107019-107019
Closed Access | Times Cited: 1
Early Detection of Patients with Mild Cognitive Impairment through EEG-SSVEP-based Machine Learning Model
Dohyun Kim, Jinseok Park, Hojin Choi, et al.
IEEE Access (2024) Vol. 12, pp. 172101-172114
Open Access | Times Cited: 1
Comparison analysis between standard polysomnographic data and in-ear-EEG signals:A preliminary study
Gianpaolo Palo, Luigi Fiorillo, Giuliana Monachino, et al.
SLEEP Advances (2024) Vol. 5, Iss. 1
Open Access
Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment
Yosef Bernardus Wirian, Yang Jiang, Sylvia Cerel‐Suhl, et al.
Lecture notes in networks and systems (2023), pp. 81-90
Open Access
[The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].
Yi Liu, Zhenyang Li, Zhiwei Wei, et al.
PubMed (2022) Vol. 39, Iss. 6, pp. 1233-1239
Closed Access
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