ArticleDiagnostics (Basel, Switzerland)2022
Parkinson's Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.Cognitive neurodynamics · 2026Article
- A Hybrid Random Forest-SARSA Framework for Resting-State EEG-Based Parkinson's Disease Detection With Temporal Decision Refinement.Brain and behavior · 2026Article
- Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression.GeroScience · 2026Article
- Exploring Dynamic Alpha Band Connectivity in Parkinson's Disease: A Novel Approach to Postural Control Assessment Using the BioVRSea Paradigm.Brain topography · 2026Article
- Stimulus-Evoked Brain Signals for Parkinson's Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments.Bioengineering (Basel, Switzerland) · 2025Article
- Independent component analysis of oddball EEG recordings to detect Parkinson's disease.Scientific reports · 2025Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Voice biomarkers as prognostic indicators for Parkinson's disease using machine learning techniques.Scientific reports · 2025Article
- A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson's Disease Detection from Electroencephalogram Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion.PloS one · 2025Article
- EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method.Diagnostics (Basel, Switzerland) · 2023Article
- Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.Frontiers in neurology · 2023Article
- EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.Frontiers in human neuroscience · 2023Article
- Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.Scientific reports · 2022Article
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Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson's disease in two cases, off-medication and on-medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy.
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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.