# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 3 Results and Discussion
### Real-Time Application  
In this subsection, the fuzzy tracking and control algorithm was applied to control an air swimmer drone vehicle for subjects with ALS in entertainment. The air swimmer drone vehicle (Figure 10) could be controlled to swim through the air with incredibly smooth and life-like swimming motions. The BCI layout (Figure 2) was displayed on a 19-inch LCD monitor (4:3), and the average distance  
Figure 8: Experimental results for different stimulus signals.  
Figure 9: Electroencephalography (EEG) amplitude spectra corresponding to five different stimulus frequencies.  
between two centers of two adjacent flickering boxes was 13.22 cm. The range of the input triangle membership function was 40, and the range of the output triangle membership function was 28. An example of stimulus frequency response for a subject is shown in Figure 11a. In this example, a subject was asked to control the air swimmer drone vehicle with a sequence of stimulation frequencies that correspond to the commands \"dive\" (6 Hz), \"turn left\" (7.5 Hz), \"go forward\" (8.57 Hz), \"turn right\" (6.67 Hz), and \"elevate\" (10 Hz). The subject was requested to focus on a single command for a duration of five seconds, and EEG data of the subject was acquired every second without any time interval in between two adjacent trials. The corresponding recognition results are shown in Figure 11b. The results revealed that the proposed approach can successfully convert the SSVEP EEG signals into corresponding real-time control signals.  
To evaluate the proposed approach in a real-time application, five subjects with ALS (four males and one female; age between 21 and 37) were asked to operate the air swimmer drone vehicle. For each subject, the rate of progression was measured using a revised ALS functional rating scale (ALSFRS-R) [25]. The scale of ALSFRS-R is composed of 12 items graded from 0 (complete loss of function) to 4 (normal function), with a score range between 0 (unable to perform the tested functions) and produces a score between 48 (normal function) and 0 (severe disability). The average ALSFRS-R  
Figure 11: Steady-state visual evoked potential (SSVEP) EEG signal is converted to a control signal. (**a**) The power energy of a subject’s EEG response for different stimulus frequency. (**b**) Decision results.  
Figure 10: User controlling an air swimmer drone vehicle through the proposed approach.  
score of five subjects was 29.8. The participants gave informed consent, and the study was approved by Institutional Review Board of National Cheng Kung University Hospital.  
Since the proposed application is to relax subjects with ALS by operating the air swimmer drone vehicle, the entertainment value is more important than the recognition rate. Thus, to quantify the entertainment value of the proposed system, the caregivers helped us to ask each subject to answer the question \"Is the system interesting?\" Then, each subject was asked to provide a mean opinion score (MOS) ranging from 5 (_excellent_) to 1 (_unsatisfactory_). The average MOS was 4.3. Thus, the subjects' responses indicate that the proposed system can hold the interest of subjects with ALS.