Abstract
Developments in Brain-Computer Interface machines have provided researchers with the opportunity to interface with robotics and artificial intelligence; and, each BCI—Robotics system employed different Machine Learning algorithms. This study aimed to present a performance analysis for a Neuro-Fuzzy algorithm, specifically the Adaptive Network-Fuzzy Inference System (ANFIS), to classify EEG signals retrieved by the Emotiv INSIGHT in conjunction with the SVM algorithm as reference. Generation of EEG data was done through face gestures, specifically Facial and Eye Gestures. The generated data were fed to both algorithms for simulation experiments. Results showed that the ANFIS tends to be more reliable and marginally better than the SVM algorithm. Compared to SVM, the ANFIS took significant amounts of computational resources requiring higher specs and training time.
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Acknowledgments
This work was presented in dissertation form in fulfillment of the requirements for the M.Sc. in Robotics Engineering for Timothy Chu under the supervision of E.L. Secco from the Robotics Laboratory, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, UK, and Dr. Alvin Chua from the Mechanical Engineering Department, De La Salle University, PH.
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Chu, T.S.C., Chua, A., Secco, E.L. (2022). Performance Analysis of a Neuro-Fuzzy Algorithm in Human-Centered and Non-invasive BCI. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_22
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DOI: https://doi.org/10.1007/978-981-16-2380-6_22
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