3 research outputs found
Ongoing EEG artifact correction using blind source separation
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.Comment: 16 pages, 4 figures, 3 table