A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

Rashid, Mamunur and Bari, Bifta Sama and Norizam, Sulaiman and Mahfuzah, Mustafa and Md Jahid, Hasan and Islam, Md Nahidul and Naziullah, Shekh (2021) A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology. SN Applied Sciences, 3 (9). pp. 1-14. ISSN 2523-3971. (Published)

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The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: SSVEP; Brain-Computer Interface; BCI; Electroencephalography; EEG; Machine Learning
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Electrical and Electronic Engineering Technology
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 10 Feb 2022 02:38
Last Modified: 10 Feb 2022 02:38
URI: http://umpir.ump.edu.my/id/eprint/32204
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