|Title:||Wireless stimulus-on-device design for novel P300 hybrid brain-computer interface applications||Authors:||Kuo C.-H.
|Keywords:||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||Issue Date:||2018||Journal Volume:||2018||Source:||Computational Intelligence and Neuroscience||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.
|Appears in Collections:||機械工程學系|
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