Fast and effective removal of contamination from scalp electrical recordings |
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| Affiliation: | 1. College of Science and Engineering, Flinders University, Adelaide, Australia;2. Medical Device Research Institute, Flinders University, Adelaide, Australia;3. Department of Neurology, Flinders Medical Centre, Adelaide, Australia;4. Centre for Neuroscience, College of Medicine and Public Health, Flinders University, Adelaide, Australia;1. Department of Neurology, Innsbruck, Medical University, Innsbruck, Austria;2. Johns Hopkins Bayview Neurology, Baltimore, MD, USA;3. Department of Neurology, Christian-Doppler-Klinik, Paracelsus Medical University of Salzburg, Salzburg, Austria;1. Department of Neurobiology, Nanjing Medical University, Nanjing 210029, People’s Republic of China;2. Key Laboratory of Developmental Genes and Human Disease, MOE, Institute of Life Sciences, Southeast University, Nanjing 210096, People’s Republic of China;3. Department of Pharmacology, Xuzhou Medical College, Xuzhou 221004, People’s Republic of China;1. Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy;2. IRCCS Neuromed, Pozzilli, IS, Italy;3. Department of Neurosciences, Mental Health and Sensory Organs, Suicide Prevention Center, Sant''Andrea Hospital, Sapienza University of Rome, Rome, Italy |
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| Abstract: | ObjectiveTo present a new, automated and fast artefact-removal approach which significantly reduces the effect of contamination in scalp electrical recordings.MethodWe 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.ResultsThe 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.ConclusionsThe proposed approach is the best choice for artefact reduction of scalp electrical recordings. Further improvements are possible with improved component analysis algorithms.SignificanceThis paper provides a definitive answer to an important question: Which artefact removal algorithm should be used on scalp electrical recordings? |
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| Keywords: | Scalp electrical recordings Canonical Correlation Analysis Spectral gradient Correlation coefficient Artefact removal |
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