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

Abstract 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 ne...

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Main Authors: Mamunur Rashid, Bifta Sama Bari, Norizam Sulaiman, Mahfuzah Mustafa, Md Jahid Hasan, Md Nahidul Islam, Shekh Naziullah
Format: Article
Language:English
Published: Springer 2021-08-01
Series:SN Applied Sciences
Subjects:
BCI
EEG
Online Access:https://doi.org/10.1007/s42452-021-04762-7
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spelling doaj-e2e916e4e7214044983acc62cff1bf462021-08-29T11:17:37ZengSpringerSN Applied Sciences2523-39632523-39712021-08-013911410.1007/s42452-021-04762-7A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technologyMamunur Rashid0Bifta Sama Bari1Norizam Sulaiman2Mahfuzah Mustafa3Md Jahid Hasan4Md Nahidul Islam5Shekh Naziullah6Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP)Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP)Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP)Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP)Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang (UMP)Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP)Computer Science and Engineering, Daffodil International UniversityAbstract 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.https://doi.org/10.1007/s42452-021-04762-7SSVEPBrain-Computer InterfaceBCIElectroencephalographyEEGMachine Learning
collection DOAJ
language English
format Article
sources DOAJ
author Mamunur Rashid
Bifta Sama Bari
Norizam Sulaiman
Mahfuzah Mustafa
Md Jahid Hasan
Md Nahidul Islam
Shekh Naziullah
spellingShingle Mamunur Rashid
Bifta Sama Bari
Norizam Sulaiman
Mahfuzah Mustafa
Md Jahid Hasan
Md Nahidul Islam
Shekh Naziullah
A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
SN Applied Sciences
SSVEP
Brain-Computer Interface
BCI
Electroencephalography
EEG
Machine Learning
author_facet Mamunur Rashid
Bifta Sama Bari
Norizam Sulaiman
Mahfuzah Mustafa
Md Jahid Hasan
Md Nahidul Islam
Shekh Naziullah
author_sort Mamunur Rashid
title A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
title_short A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
title_full A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
title_fullStr A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
title_full_unstemmed A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
title_sort hybrid environment control system combining emg and ssvep signal based on brain-computer interface technology
publisher Springer
series SN Applied Sciences
issn 2523-3963
2523-3971
publishDate 2021-08-01
description Abstract 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.
topic SSVEP
Brain-Computer Interface
BCI
Electroencephalography
EEG
Machine Learning
url https://doi.org/10.1007/s42452-021-04762-7
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