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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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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