Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications

• Published in: Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 13
• Main Authors: Chen, Ping-Nan, Chou, Hung-Chyun, Chen, Hung-Hsuan, Kuo, Chung-Hsien, Kuo, Yu-Cheng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Animals; Bees; Biomedical engineering; Brain; Brain - physiology; Brain injury; Brain research; Brain-Computer Interfaces; Calibration; Care and treatment; Computer applications; Design and construction; Discriminant analysis; Electroencephalography - instrumentation; Engineering; Equipment Design; Event-related potentials; Event-Related Potentials, P300; Evoked Potentials, Visual; Feature extraction; Female; Fuzzy Logic; Fuzzy systems; Head injuries; Human-computer interface; Human-machine systems; Humans; Implants; Implants, Artificial; Information transfer; Interfaces; Kalman filters; Latency; Male; Models, Biological; Neural networks; Patient outcomes; Pattern Recognition, Automated - methods; People with disabilities; Performance evaluation; Prosthesis; Quality of life; Signal Processing, Computer-Assisted; Spinal cord injuries; Stimuli; Support Vector Machine; Support vector machines; Swarm intelligence; Synchronism; Synchronization; User training; Variation; Visual Perception - physiology; Wireless communications; Wireless networks; Wireless Technology; Young Adult
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2018/2301804

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).