ArticleComputational intelligence and neuroscience2022
EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A PRISMA-based systematic review on advances in identity recognition and authentication using human biometric signals (2018-2023).Biomedical engineering online · 2026Pooled it
- Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.Sensors (Basel, Switzerland) · 2025Pooled it
- Quantum-inspired optimization of transformer-capsule networks for accurate brain tumor segmentation and classification.Scientific reports · 2026Article
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Efficient EEG-Based Person Identification: A Unified Framework from Automatic Electrode Selection to Intent Recognition.Sensors (Basel, Switzerland) · 2026Article
- A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases.Diagnostics (Basel, Switzerland) · 2022Article
- Novel DERMA Fusion Technique for ECG Heartbeat Classification.Life (Basel, Switzerland) · 2022Article
- EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm.Sensors (Basel, Switzerland) · 2022Article
- RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm.Computational intelligence and neuroscience · 2022Article
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9 authors.
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Abstract
Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.
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