Azin S Janani1, Tyler S Grummett2, Hanieh Bakhshayesh3, Trent W Lewis3, Dylan DeLosAngeles4, Emma M Whitham5, John O Willoughby6, Kenneth J Pope3. 1. College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia. Electronic address: azinsadat.janani@flinders.edu.au. 2. College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia; Centre for Neuroscience, College of Medicine and Public Health, Flinders University, Adelaide, Australia. 3. College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia. 4. Centre for Neuroscience, College of Medicine and Public Health, Flinders University, Adelaide, Australia. 5. Department of Neurology, Flinders Medical Centre, Adelaide, Australia. 6. Department of Neurology, Flinders Medical Centre, Adelaide, Australia; Centre for Neuroscience, College of Medicine and Public Health, Flinders University, Adelaide, Australia.
Abstract
OBJECTIVE: To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings. METHOD: We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components. We trained two binary support vector machine classifiers such that one separates brain components from muscle components, and the other separates brain components from mains power and environmental components. We compared the performance of the proposed approach with seven currently used alternatives on three datasets, measuring mains power artefact reduction, muscle artefact reduction and retention of brain neurophysiological responses. RESULTS: The proposed approach significantly reduces the main power and muscle contamination from scalp electrical recording without affecting brain neurophysiological responses. None of the competitors outperformed the new approach. CONCLUSIONS: The proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms. SIGNIFICANCE: This paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings?
OBJECTIVE: To present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings. METHOD: We used spectral and temporal characteristics of different sources recorded during a typical scalp electrical recording in order to improve a fast and effective artefact removal approach. Our experiments show that correlation coefficient and spectral gradient of brain components differ from artefactual components. We trained two binary support vector machine classifiers such that one separates brain components from muscle components, and the other separates brain components from mains power and environmental components. We compared the performance of the proposed approach with seven currently used alternatives on three datasets, measuring mains power artefact reduction, muscle artefact reduction and retention of brain neurophysiological responses. RESULTS: The proposed approach significantly reduces the main power and muscle contamination from scalp electrical recording without affecting brain neurophysiological responses. None of the competitors outperformed the new approach. CONCLUSIONS: The proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms. SIGNIFICANCE: This paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings?
Authors: Kenneth J Pope; Trent W Lewis; Sean P Fitzgibbon; Azin S Janani; Tyler S Grummett; Patricia A H Williams; Malcolm Battersby; Tarun Bastiampillai; Emma M Whitham; John O Willoughby Journal: Brain Behav Date: 2022-08-02 Impact factor: 3.405