Performance Analysis of a Neuro Fuzzy Algorithm in Human Centered & Non-Invasive BCI

Chu, Timothy and Chua, Alvin and Secco, Emanuele Lindo (2020) Performance Analysis of a Neuro Fuzzy Algorithm in Human Centered & Non-Invasive BCI. In: Sixth International Congress on Information and Communication Technology (ICICT). (Accepted for Publication)

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Abstract

Brain-Computer Interface can be non-invasive devices that obtain sig-nals generated from the brain and are then manipulated to suit various applica-tions. A popular application for BCI is interfacing with robotics; and, each BCI – Robotics system employed different Machine Learning algorithms. This study aimed to present a performance analysis for a Neuro-Fuzzy algorithm, specifi-cally the Adaptive-Network-Fuzzy-Inference System (ANFIS), to classify EEG signals retrieved by the Emotiv INSIGHT. An SVM algorithm is also developed to serve as a reference vs the ANFIS’s performance. A methodology for genera-tion and acquisition of EEG signals can be used by researchers as reference. Fa-cial and Eye Gestures were utilized as means of EEG signal generation which are fed to both algorithms for simulation experiments. Results showed that the ANFIS tend to be more reliable and marginally better than of the SVM algorithm. Compared to SVM, the ANFIS took significant amounts of computational re-sources requiring higher specs and training time.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: This is the accepted version of a paper that will be presented at the Sixth International Congress on Information and Communication Technology (ICICT), February 2021.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Emanuele Secco
Date Deposited: 23 Nov 2020 15:46
Last Modified: 27 Feb 2021 01:15
URI: https://hira.hope.ac.uk/id/eprint/3183

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