Evaluation of a minimum-norm based beamforming technique, sLORETA, for reducing tonic muscle contamination of EEG at sensor level

Azin Janani, Tyler Grummett, Trent Lewis, Sean Fitzgibbon, Emma Whitham, Dylan DeLosAngeles, Hanieh Bakhshayesh, John Willoughby, Kenneth Pope

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    Background Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100 Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed. New method In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level. Using a generic volume conduction model of the head, which includes three layers (brain, skull, and scalp), and sLORETA, we estimated time-series of sources distributed within the brain and scalp. The sources within the scalp were considered to be muscle and discarded in forward modelling. Result (1) The method reduced EMG contamination, more strongly at peripheral channels; (2) task-induced cortical activity was retained or revealed after removing putative muscle activity. Comparison with existing methods This approach can decrease tonic muscle contamination in scalp measurements without relying on time-consuming processing of expensive MRI data. In addition, it is competitive to ICA in muscle reduction and can be reliably applied on any length of recorded data that captures the dynamics of the signals of interest. Conclusion This study suggests that sLORETA can be used as a method to quantitate cranial muscle activity and reduce its contamination at sensor level.

    Original languageEnglish
    Pages (from-to)17-28
    Number of pages12
    JournalJournal of Neuroscience Methods
    Volume288
    DOIs
    Publication statusPublished - 15 Aug 2017

    Keywords

    • Beamformer
    • Electroencephalogram
    • Electromyogram
    • Forward modelling
    • Inverse modelling
    • Neurophysiological response

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