NDLI: Classifying real and imaginary finger press tasks on a P300-based brain-computer interface

Content Provider	IEEE Xplore Digital Library
Author	Jicai Zhang Weidong Chen Yanlei Gu Bian Wu Yu Qi Xiaoxiang Zheng
Copyright Year	2011
Description	Author affiliation: QAAS and Department of of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China (Yanlei Gu; Bian Wu) \|\| QAAS and College of Computer Science, Zhejiang University, Hangzhou 310027, China (Weidong Chen) \|\| QAAS and Key lab. of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China (Xiaoxiang Zheng) \|\| Qiushi Academy for Advanced Studies (QAAS) and College of Computer Science, Zhejiang University, Hangzhou 310027, China (Jicai Zhang; Yu Qi)
Abstract	Brain computer interfaces based on P300 and sensory-motor rhythms are widely studied and recent advances show some interest in the combination of the two. In this paper, typical P300 paradigm is modified by adding animation guide of the finger press as a stimulus and by using different response strategies (silent counting and actual/imaginary left or right index finger press following the animation). Both P300 potentials and sensory-motor rhythms are directly exploited and discussed. Classification results showed that even under very demanding conditions, which was, 200ms inter-stimulus interval of the P300 stimuli and actual/imaginary finger press once per 1.6s, the paradigm can evoke both P300 potentials and sensory-motor rhythms simultaneously. Actual finger press increased single trial P300 selection accuracy of different subjects by 5–29.5% compared with silent counting; imaginary finger press did not increase the P300 selection accuracy apparently for most subjects except the two who were very poor at counting task. This showed by using different interface design and adopting certain mental response strategies, the ‘BCI illiteracy’ may be cured. Also imaginary task had good performance of left versus right classification (with the best subject reached 81.1% of accuracy), which is an additional information that can be used to improve system performance.
Starting Page	6356
Ending Page	6359
File Size	433740
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091569
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Presses Thumb Accuracy Electroencephalography Feature extraction Animation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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