ARTIST: A fully automated artifact rejection algorithm for single‐pulse TMS‐EEG data

Concurrent single‐pulse TMS‐EEG (spTMS‐EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS‐EEG data suffer from enormous stimulation‐induced artifacts, posing significant challenges to the extracti...

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Bibliographic Details
Published in:Human brain mapping 2018-04, Vol.39 (4), p.1607-1625
Main Authors: Wu, Wei, Keller, Corey J., Rogasch, Nigel C., Longwell, Parker, Shpigel, Emmanuel, Rolle, Camarin E., Etkin, Amit
Format: Article
Language:English
Subjects:
Adult
Algorithms
Artifact identification
artifact rejection
Attention deficit hyperactivity disorder
Automation
Brain
Cleaning
EEG
electroencephalogram
Electroencephalography - methods
Female
Humans
Male
Meditation
Nervous system
Pattern Recognition, Automated - methods
Rejection
Signal Processing, Computer-Assisted
Statistical analysis
Stimulation
transcranial magnetic stimulation
Transcranial Magnetic Stimulation - methods
Waveforms
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Summary:Concurrent single‐pulse TMS‐EEG (spTMS‐EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS‐EEG data suffer from enormous stimulation‐induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time‐intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS‐EEG artifact rejection. A key step of this algorithm is to decompose the spTMS‐EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio‐temporal profile of both neural and artefactual activities. The autocleaned and hand‐cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS‐EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS‐EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS‐EEG data, which can increase the utility of TMS‐EEG in both clinical and basic neuroscience settings.
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.23938