TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

Autor: Giacomo Koch, John C. Rothwell, Alessandra Bertoldo, Gianna Toffolo, Vincenza Tarantino, Elias Paolo Casula, Patrizia Silvia Bisiacchi, Michele Maiella
Přispěvatelé: Casula, Elias P., Bertoldo, Alessandra, Tarantino, Vincenza, Maiella, Michele, Koch, Giacomo, Rothwell, John C., Toffolo, Gianna M., Bisiacchi, Patrizia S.
Rok vydání: 2017
Předmět:
Male
Time Factors
medicine.medical_treatment
Electroencephalography
Signal
0302 clinical medicine
Signal correction
Detrend
EEG
Adaptive algorithm
medicine.diagnostic_test
05 social sciences
Transcranial Magnetic Stimulation
Sensory Systems
Algorithm
Neurology
Artefact
ICA
TMS
Neurology (clinical)
Physiology (medical)
Artifact
Female
Primary motor cortex
Artifacts
Psychology
Algorithms
Human
Adult
Time Factor
050105 experimental psychology
NO
Young Adult
03 medical and health sciences
medicine
Humans
Middle frontal gyrus
0501 psychology and cognitive sciences
Settore M-PSI/02 - Psicobiologia E Psicologia Fisiologica
business.industry
Pattern recognition
Independent component analysis
Transcranial magnetic stimulation
Artificial intelligence
Sensory System
business
Neuroscience
030217 neurology & neurosurgery
Zdroj: Clinical Neurophysiology. 128:1563-1574
ISSN: 1388-2457
DOI: 10.1016/j.clinph.2017.06.003
Popis: Objective During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. Methods We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. Results Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. Conclusions ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. Significance We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts.
Databáze: OpenAIRE
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