Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI SystemReport as inadecuate


Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System


Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System - Download this document for free, or read online. Document in PDF available to download.

1

Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan

2

Department of Computer and Communication, Kun-Shan University, Tainan 710, Taiwan





*

Author to whom correspondence should be addressed.



Academic Editor: Stephen D. Prior

Abstract Subjects with amyotrophic lateral sclerosis ALS consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface BCI application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography EEG signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis CCA was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes. View Full-Text

Keywords: brain-computer interface; steady-state visual evoked potentials; fuzzy logic; CCA brain-computer interface; steady-state visual evoked potentials; fuzzy logic; CCA





Author: Yeou-Jiunn Chen 1, Shih-Chung Chen 1, Ilham A. E. Zaeni 1 and Chung-Min Wu 2,*

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents