Machine learning validation of EEG+tACS artefact removal.
Journal:
Journal of neural engineering
PMID:
31739290
Abstract
OBJECTIVE: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed.