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Appl. Sci. 2016, 6(10), 270; doi:10.3390/app6100270

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

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
Received: 27 May 2016 / Revised: 12 September 2016 / Accepted: 14 September 2016 / Published: 22 September 2016
View Full-Text   |   Download PDF [3070 KB, uploaded 22 September 2016]   |  

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, Y.-J.; Chen, S.-C.; Zaeni, I.A.E.; Wu, C.-M. Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System. Appl. Sci. 2016, 6, 270.

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