A new algorithm for blink correction adaptive to inter- and intra-subject variability.

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2019
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Abstract
Electroencephalographic (EEG) signals are constantly superimposed with biological artifacts. In particular, spontaneous blinks represent a recurrent event that cannot be easily avoided. The main goal of this paper is to present a new algorithm for blink correction (ABC) that is adaptive to inter- and intra-subject variability. The whole process of designing a Brain-Computer Interface (BCI)-based EEG experiment is highlighted. From sample size determination to classification, a mixture of the standardized low-resolution electromagnetic tomography (sLORETA) for source localization and time restriction, followed by Riemannian geometry classifiers is featured. Comparison between ABC and the commonly-used Independent Component Analysis (ICA) for blinks removal shows a net amelioration with ABC. With the same pipeline using uncorrected data as a reference, ABC improves classification by 5.38% in average, whereas ICA deteriorates by -2.67%. Furthermore, while ABC accurately reconstructs blink-free data from simulated data, ICA yields a potential difference up to 200% from the original blink-free signal and an increased variance of 30.42%. Finally, ABC's major advantages are ease of visualization and understanding, low computation load favoring simple real-time implementation, and lack of spatial filtering, which allows for more flexibility during the classification step.
Reference Key
guttmannflury2019acomputers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guttmann-Flury, E;Sheng, X;Zhang, D;Zhu, X;, ;
Journal Computers in biology and medicine
Year 2019
DOI
S0010-4825(19)30319-1
URL
Keywords
EEG electroencephalography bci independent component analysis artifact removal brain computer interface ica riemannian geometry sample size calculation spontaneous blinking standardized low-resolution electromagnetic tomography sloreta

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