P300-based Brain–Computer Interface with Latency Estimation Using ABC-based Interval Type-2 Fuzzy Logic System

• Published in: International journal of fuzzy systems Vol. 19; no. 2; pp. 529 - 541
• Main Authors: Kuo, Chung-Hsien, Kuo, Yu-Cheng, Chou, Hung-Chyun, Lin, Yi-Tseng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bats; Computational Intelligence; Engineering; Feature extraction; Fuzzy logic; Fuzzy sets; Human-computer interface; Information transfer; Management Science; Neural networks; Noise; Operations Research; Support vector machines; Visual stimuli; Interval type-2 fuzzy logic system; Steady-state visually evoked potential; P300; Artificial bee-colony algorithm; Brain–computer interface
• ISSN: 1562-2479; 2199-3211
• DOI: 10.1007/s40815-016-0205-x

P300 is a brain–computer interface (BCI) modality which reflects brains’ processes in stimulus events. Visual stimuli are usually used to elicit event-related P300 components. However, depending on different subjects’ conditions and their current cerebral loads, P300 components occur at posterior to stimuli from 250 to 600 ms roughly. These subjects’ dependent variations affect the performance of BCI. Thus, an estimation model that estimated an appropriate interval for P300 feature extraction is discussed in this paper. An interval type-2 fuzzy logic system trained by artificial bee-colony algorithm was used to find the latency of elicited P300 with a certain range by means of steady-state visually evoked potential. A support vector machine classifier was adopted to classify extracted epochs into target and non-target stimuli. Seven subjects were involved in experiments. Results showed that the performance of information transfer rate was improved by 1.28 % on average if the proposed latency-estimation approach was introduced.