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Updated: Oct 7, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.

David E Thompson1, Md Rakibul Mowla1, Katie J Dhuyvetter1

  • 1Brain and Body Sensing (BBS) Lab, Mike Wiegers Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA.

Brain Computer Interfaces (Abingdon, England)
|January 6, 2022
PubMed
Summary
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Automated artifact removal methods for electroencephalogram (EEG) brain-computer interfaces (BCIs) often degrade performance. Even the best methods significantly reduced P3 Speller BCI accuracy, highlighting challenges in EEG signal processing for assistive technologies.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-Computer Interfaces (BCIs) offer communication and control for individuals with severe paralysis.
  • Non-invasive electroencephalogram (EEG)-based BCIs are susceptible to significant noise from artifacts like electro-oculogram (EOG).
  • Artifacts can be substantially larger than the neural signals of interest, complicating data interpretation.

Purpose of the Study:

  • To compare the performance of ten different automated artifact removal methods for EEG.
  • To evaluate the impact of these methods on P3 Speller Brain-Computer Interface (BCI) performance.

Main Methods:

  • Implementation and testing of ten distinct automated artifact removal algorithms.
  • Application of these algorithms to EEG recordings used for a P3 Speller BCI.
Keywords:
Brain-computer interfacesP300 spellerartifacts rejectionphysiological signalssignal processing

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  • Quantitative assessment of BCI accuracy before and after artifact removal.

    • Main Results:

    • All tested automated artifact removal methods significantly reduced P3 Speller BCI performance.
    • Methods were more likely to decrease BCI accuracy than to improve it.
    • SOBI, JADER, and EFICA were the least detrimental, yet still caused an average ~10% drop in BCI accuracy.

    Conclusions:

    • Current automated artifact removal techniques may not be suitable for enhancing EEG-based BCI performance.
    • The empirical reduction in BCI accuracy suggests potential mechanistic issues with these signal processing methods.
    • Further research is needed to develop artifact removal strategies that preserve or improve BCI efficacy.