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