ArticleFrontiers in neuroscience2020
EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review.Neurology international · 2025Review
- EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
- EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.Brain informatics · 2024Article
- An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.Scientific reports · 2024Article
- Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.Brain sciences · 2023Article
- Seizure detection with reduced electroencephalogram channels: research trends and outlook.Royal Society open science · 2023Review
- Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2022Review
- Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization.Scientific reports · 2022Article
- Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm.Scientific reports · 2022Article
- DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.Diagnostics (Basel, Switzerland) · 2022Article
- EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.Computational intelligence and neuroscience · 2022Article
- Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation.Frontiers in aging neuroscience · 2022Article
- A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction.Sensors (Basel, Switzerland) · 2021Article
- Seizure Prediction in EEG Signals Using STFT and Domain Adaptation.Frontiers in neuroscience · 2021Article
- Efficient EEG channel-and-frequency-band selection for epileptic seizure classification using multi-objective optimization.Frontiers in neurologyArticle
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2 authors.
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Abstract
We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values. The obtained feature vectors are then iteratively tested for solving two unconstrained objectives by NSGA-II or NSGA-III; to maximize classification accuracy and to reduce the number of EEG channels required for epileptic seizure classification. Our results have shown accuracies of up to 1.00 with only one EEG channel. Interestingly, when using all the EEG channels available, lower accuracies were achieved compared to the case when EEG channels were selected by NSGA-II or NSGA-III; i.e., in patient 19 we obtained an accuracy of 0.95 using all the channels and 0.975 using only two channels selected by NSGA-III. The results obtained are encouraging and it has been shown that it is possible to classify epileptic seizures using a few electrodes, which provide evidence for the future development of portable EEG seizure detection devices.
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