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Probability mapping based artifact detection and removal from single-channel EEG signals for brain-computer interface

Md Kafiul Islam1, Parviz Ghorbanzadeh2, Amir Rastegarnia3

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Journal of Neuroscience Methods
|June 17, 2021
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Summary

Artifacts in electroencephalogram (EEG) signals degrade brain-computer interface (BCI) performance. This study introduces a novel method for detecting and removing EEG artifacts, significantly improving BCI accuracy in real-time applications.

Keywords:
Artifact removalBCIEEGWavelet transform

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Artifacts in electroencephalogram (EEG) signals significantly impair the performance of subsequent analysis algorithms, particularly in brain-computer interface (BCI) applications.
  • Effective artifact detection and removal are crucial for reliable EEG-based decision-making and BCI classification.

Purpose of the Study:

  • To develop and validate a novel approach for detecting and removing artifacts from single-channel EEG signals.
  • To enhance the performance of BCI systems by improving the quality of processed EEG data.

Main Methods:

  • A novel artifact detection method using four statistical measures: entropy, kurtosis, skewness, and periodic waveform index.
  • Artifact removal employing stationary wavelet transform, guided by a user-defined probability threshold for artifactual epochs.

Main Results:

  • The proposed method demonstrates superior performance in suppressing artifacts from both synthetic and real EEG data with minimal distortion.
  • Evaluation on BCI datasets shows that artifact removal substantially improves BCI output for both event-related potential and motor-imagery tasks.

Conclusions:

  • The developed algorithm offers a robust and efficient solution for EEG artifact removal.
  • This work is expected to advance real-time EEG signal processing and contribute to the development of more accurate BCI applications.