P300-based Brain–Computer Interface with Latency Estimation Using ABC-based Interval Type-2 Fuzzy Logic System
Journal
International Journal of Fuzzy Systems
Journal Volume
19
Journal Issue
2
Pages
529-541
Date Issued
2017
Author(s)
Abstract
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. ? 2016, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg.
Subjects
Bioelectric potentials
Computer circuits
Evolutionary algorithms
Feature extraction
Fuzzy logic
Interface states
Optimization
Artificial bee colony algorithms
Information transfer rate
Interval type-2 fuzzy logic systems
Latency estimation
P300
Steady state visually evoked potentials
Support vector machine classifiers
Target and non targets
Brain computer interface
Type
journal article