Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Front Hum Neurosci. 2023 Oct 18:17:1251690. doi: 10.3389/fnhum.2023.1251690. eCollection 2023.

Abstract

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

Keywords: artifact removal; brain-computer interface (BCI); cable swing; electroencephalography (EEG); fasciculation; motion artifact; muscle artifact.

Publication types

  • Review

Grants and funding

This study has not received any additional project funding that needs to be declared here, but has received direct support through a cooperation of Salzburg Research and TU Graz. Open access funding provided by Graz University of Technology Open Access Publishing Fund.