• Timothy Scott Chu Department of Mechanical Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Alvin Chua Department of Mechanical Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Emanuele Lindo Secco Robotics Laboratory, School of Mathematics, Computer Science & Engineering, Liverpool Hope University, United Kingdom




ANFIS, Brain Computer Interface, Quadcopter, SVM, UAV


Brain-Computer Interface (BCI) machines are capable of obtaining brain activities by conducting Electroencephalogram tests. Developments on both BCI and Machine Learning allowed various researchers to develop and study various BCI control systems, mainly varying with the algorithm implementation. This research presents a performance analysis of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for BCI control systems for drone maneuverability. Eye gestures were used to generate the EEG data that were captured using the Emotiv INSIGHT Neuroheadset. The obtained data were transferred to the computing hardware using IEEE 802.15 wirless communication protocol (i.e. Bluetooth connectivity); the data are processed using the 5th order Butterworth Band-Pass filtering and heuristic filtering. The filtered dataset is then fed to the ANFIS and a Support Vector Machine (SVM) algorithm, the latter serving as the basis, for training and quadcopter control implementation. Three flight tests were done, hover test, flight command test, and the flight control test, the final test compared the performance of the BCI control system using the ANFIS algorithm to the performance of a traditional handheld remote controller. Results from the initial two tests showed that the ANFIS performed comparably with the SVM, and even about 2% better. The final test showed that the BCI control system had a maximum variance of 4% compared to the handheld controller, where the latter served as the basis. It was found that between Machine Learning algorithms, ANFIS is as capable as the SVM for BCI control systems. Further developments may focus on employing time-series EEG preprocessing techniques.


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How to Cite

Chu, T. S. ., Chua, A. ., & Secco, E. L. . (2022). A STUDY ON NEURO FUZZY ALGORITHM IMPLEMENTATION ON BCI-UAV CONTROL SYSTEMS. ASEAN Engineering Journal, 12(4), 75-81. https://doi.org/10.11113/aej.v12.16900