ArticleCognitive neurodynamics2023
Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.
Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- A Consolidated Framework for the Detection of Alzheimer's Disease Using EEG Signals and Hybrid Models.Biomimetics (Basel, Switzerland) · 2026Article
- TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.Diagnostics (Basel, Switzerland) · 2026Article
- BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images.Cognitive neurodynamics · 2025Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.Scientific reports · 2025Article
- An ensemble model based on transfer learning for the early detection of Alzheimer's disease.Scientific reports · 2025Article
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.BMC medical imaging · 2025Article
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia.Frontiers in medicine · 2025Article
- Alzheimer's disease image classification based on enhanced residual attention network.PloS one · 2025Article
- Electroencephalogram-based time-frequency analysis for Alzheimer's disease detection using machine learning.Journal of biological methods · 2025Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer's disorder detection using EEG signals.Health information science and systems · 2024Article
- Creativity and Mental Illness: A Case Study of a Patient with Progressive Bulbar Palsy.Brain sciences · 2024Review
- Artificial intelligence for brain disease diagnosis using electroencephalogram signals.Journal of Zhejiang University. Science. B · 2024Review
- Lattice 123 pattern for automated Alzheimer's detection using EEG signal.Cognitive neurodynamics · 2024Article
- Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals.Cognitive neurodynamics · 2024Article
- Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.
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