Ongoing EEG artifact correction using blind source separation

TL;DR

This paper presents a fast, automatic blind source separation algorithm for real-time EEG artifact correction, significantly improving artifact removal efficiency while maintaining EEG signal integrity for online applications.

Contribution

The study introduces a novel, fast blind source separation method with a sliding window for continuous EEG artifact correction applicable offline and online.

Findings

88% artifact removal success rate

Outperforms existing algorithms in speed and accuracy

Effective for ocular, cardiac, muscle, and powerline artifacts

Abstract

Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. Results: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.