Literature DB >> 17466588

Effect of eye-blinks on a self-paced brain interface design.

Ali Bashashati1, Borna Nouredin, Rabab K Ward, Peter Lawrence, Gary E Birch.   

Abstract

OBJECTIVE: To test the performance of an EEG-based self-paced brain interface when data contaminated with eye-blink artefacts are included in the evaluation.
METHODS: Two different designs of a self-paced brain interface (the low frequency-asynchronous switch design, LF-ASD) are evaluated and compared using offline data from eight subjects. The true positive rates of the two designs are compared for three cases: (a) data containing eye-blink artefacts are excluded from the input; (b) all data, including eye-blinks, are included as input but the output decisions are inactivated during eye-blink artefacts; (c) all the data, including eye-blinks, are included as input and the output decisions are reported in all times including during eye-blink artefacts.
RESULTS: The true positive rates of one design of the LF-ASD (LF-ASD-V5) for case (c) and of another design (LF-ASD-V4) for case (b) are 40.5% and 42.4%, respectively, for false positive rates of 1%.
CONCLUSIONS: The true positive rates of LF-ASD-V5 when eye-blinks are included in the analysis deteriorate slightly compared to when the output during eye-blink artefacts is inactivated in LF-ASD-V4. SIGNIFICANCE: LF-ASD-V5 allows the device to be functional at all times and can handle artefacts better than LF-ASD-V4. If a slight decrease in true positive rates is acceptable, no further devices are needed to record the electro-oculogram (EOG) for detecting eye-blinks.

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Year:  2007        PMID: 17466588     DOI: 10.1016/j.clinph.2007.03.020

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  3 in total

Review 1.  EOG-Based Human-Computer Interface: 2000-2020 Review.

Authors:  Chama Belkhiria; Atlal Boudir; Christophe Hurter; Vsevolod Peysakhovich
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

Review 2.  Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Authors:  Keum-Shik Hong; Muhammad Jawad Khan
Journal:  Front Neurorobot       Date:  2017-07-24       Impact factor: 2.650

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

Authors:  Xinyi Yong; Mehrdad Fatourechi; Rabab K Ward; Gary E Birch
Journal:  J Neuroeng Rehabil       Date:  2012-07-27       Impact factor: 4.262

  3 in total

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