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Related Experiment Videos

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

Mathias Schmoigl-Tonis1,2, Christoph Schranz1, Gernot R Müller-Putz2,3

  • 1Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria.

Frontiers in Human Neuroscience
|November 3, 2023
PubMed
Summary

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Motion artifacts in electroencephalography (EEG) challenge brain-computer interfaces (BCIs). This review systematically analyzes methods to reduce these artifacts in online BCI experiments, guiding future research.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) leverage electroencephalography (EEG) for brain-device communication due to EEG's high temporal resolution and non-invasiveness.
  • Motion artifacts, stemming from muscle activity or cable movement, significantly degrade EEG signal quality in real-world BCI applications.
  • Effective artifact reduction is crucial for reliable online BCI performance.

Purpose of the Study:

  • To systematically review and evaluate methods for reducing motion artifacts in online electroencephalography (EEG) based brain-computer interface (BCI) experiments.
  • To identify and compare the performance and suitability of various artifact reduction techniques and pipelines.
  • To provide a comprehensive overview of the current state-of-the-art, identify research gaps, and discuss community consensus.
Keywords:
artifact removalbrain-computer interface (BCI)cable swingelectroencephalography (EEG)fasciculationmotion artifactmuscle artifact

Related Experiment Videos

Main Methods:

  • A systematic literature search was conducted using the PRISMA filter method on PubMed, focusing on open-access publications from 1966 to 2022.
  • 2,333 publications were evaluated against predefined filtering rules to identify relevant studies on motion artifact reduction in EEG data.
  • Methods, pipelines, and their performance were analyzed and compared for suitability in online BCI applications.

Main Results:

  • A lookup table was compiled, detailing all reviewed papers, employed methods, and data processing pipelines.
  • The performance and suitability of different motion artifact reduction strategies for online BCI were assessed.
  • Key findings highlight effective methods, algorithms, and concepts for real-time artifact management.

Conclusions:

  • This systematic review provides a comprehensive overview of motion artifact reduction techniques for online EEG-BCI systems.
  • The findings guide researchers in selecting appropriate methods, addressing current limitations, and highlight areas for future investigation.
  • The study aims to advance the reliability and applicability of BCI technology in real-world scenarios.