ArticleSensors (Basel, Switzerland)2022
EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals.Health information science and systems · 2023Article
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.Sensors (Basel, Switzerland) · 2023Article
- Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- 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
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Authors and funding
9 authors.
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Abstract
The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain's electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and β-Hill Climbing optimizer called FPAβ-hc. The performance of the FPAβ-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPAβ-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods.
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