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Automatic artefact removal in a self-paced hybrid brain- computer interface system.

Xinyi Yong¹, Mehrdad Fatourechi, Rabab K Ward

¹Department of Electrical and Computer Engineering, University of British Columbia, 2356 Main Mall, Vancouver, V6T1Z4 Canada. yongy@ece.ubc.ca

Journal of Neuroengineering and Rehabilitation

|July 31, 2012

Summary

This summary is machine-generated.

A new algorithm effectively removes artefacts from electroencephalogram (EEG) signals, significantly improving brain-computer interface (BCI) performance. This method enhances accuracy and reduces signal distortion for hybrid BCI systems.

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Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Brain-computer interface (BCI) systems often suffer from degraded performance due to artefacts in electroencephalogram (EEG) signals.
Hybrid BCI systems, combining EEG with eye-tracking for virtual keyboard control, are particularly susceptible to these signal interferences.
Artefacts compromise the reliability and accuracy of BCI-based communication and control.

Purpose of the Study:

To introduce a novel artefact removal algorithm for self-paced hybrid BCI systems.
To enhance the performance and reliability of BCI systems by mitigating EEG signal contamination.
To evaluate the proposed algorithm's effectiveness against existing artefact handling methods.

Main Methods:

The proposed algorithm utilizes stationary wavelet transform with an adaptive thresholding mechanism for artefact removal.
Performance was evaluated using semi-simulated EEG signals (real EEG with added artefacts) and real EEG data from seven participants.
Online-like evaluation was conducted using continuous data from the last session to simulate real-time BCI operation.

Main Results:

The algorithm demonstrated reduced signal distortion in both time and frequency domains on semi-simulated data.
For real EEG data with 0.0s dwell time, the algorithm achieved a 44.7% true positive rate (TPR), exceeding other methods by over 15.0%.
Increasing dwell time to 1.0s resulted in a TPR of 73.1%, indicating improved accuracy with longer target selection periods.

Conclusions:

The novel artefact removal algorithm significantly enhances BCI system performance.
Advantages include no need for additional channels, suitability for real-time processing, and reduced signal distortion.
The algorithm offers a practical solution for improving the usability and effectiveness of hybrid BCI systems.