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

Author:

Rashid MamunurORCID,Bari Bifta Sama,Sulaiman Norizam,Mustafa Mahfuzah,Hasan Md Jahid,Islam Md Nahidul,Naziullah Shekh

Abstract

AbstractThe 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.

Funder

Universiti Malaysia Pahang

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Physics and Astronomy,General Engineering,General Environmental Science,General Materials Science,General Chemical Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Classification of EEG-Based Auditory Evoked Potentials Using Entropy-Based Features and Machine Learning Techniques;2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD);2023-09-21

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