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  4. Wireless stimulus-on-device design for novel P300 hybrid brain-computer interface applications
 
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Wireless stimulus-on-device design for novel P300 hybrid brain-computer interface applications

Journal
Computational Intelligence and Neuroscience
Journal Volume
2018
Date Issued
2018
Author(s)
Kuo C.-H.
Chen H.-H.
Chou H.-C.
Chen P.-N.
Kuo Y.-C.
CHUNG-HSIEN KUO  
DOI
10.1155/2018/2301804
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051069125&doi=10.1155%2f2018%2f2301804&partnerID=40&md5=f1d03cb76bb34c969855df3f11690d0c
https://scholars.lib.ntu.edu.tw/handle/123456789/611559
Abstract
Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations). ? 2018 Chung-Hsien Kuo et al.
Subjects
Classification (of information)
Fuzzy logic
Interfaces (computer)
Optimization
Support vector machines
Wireless telecommunication systems
Artificial bee colonies (ABC)
Brain computer interfaces (BCIs)
Brain-computer interface applications
Information transfer rate
Interval type-2 fuzzy logic systems
Spinal cord injuries (SCI)
Target and non targets
Wireless application
Brain computer interface
animal
automated pattern recognition
bee
biological model
brain
brain computer interface
calibration
devices
electroencephalography
equipment design
event related potential
female
fuzzy logic
human
male
physiology
procedures
signal processing
support vector machine
vision
visual evoked potential
wireless communication
young adult
Animals
Bees
Brain
Brain-Computer Interfaces
Calibration
Electroencephalography
Equipment Design
Event-Related Potentials, P300
Evoked Potentials, Visual
Female
Fuzzy Logic
Humans
Male
Models, Biological
Pattern Recognition, Automated
Signal Processing, Computer-Assisted
Support Vector Machine
Visual Perception
Wireless Technology
Young Adult
Type
journal article

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