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

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Mamunur Rashid ◽  
Bifta Sama Bari ◽  
Norizam Sulaiman ◽  
Mahfuzah Mustafa ◽  
Md Jahid Hasan ◽  
...  
Keyword(s):  
Control System ◽  
Real Time ◽  
Control Method ◽  
Environmental Control ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Neural Pathways ◽  
Common Spatial Pattern ◽  
Environment Control ◽  
Bci System

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.

Hybrid EEG-EMG Based Brain Computer Interface (BCI) System For Real-Time Robotic Arm Control

2018 ◽  
Vol 10 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Saad Abdullah ◽  
◽  
Muhammad A. Khan ◽  
Mauro Serpelloni ◽  
Emilio Sardini ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Arm Control ◽  
Bci System

scholarly journals A Proposed Web Based Real Time Brain Computer Interface (BCI) System for Usability Testing

2019 ◽  
Vol 15 (08) ◽  
pp. 108
Author(s):  
Hewawasam Puwakpitiyage C. A. ◽  
Paramesura Rao V. R. ◽  
Muhammad Azizi M. S. A. ◽  
Tee W. J. ◽  
Murugesan R. K. ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Usability Testing ◽  
Computer Interface ◽  
Web Based ◽  
Expert Review ◽  
Usability Test ◽  
Usability Tests ◽  
Remote Usability Testing ◽  
Bci System

<p class="Abstract">Hallway testing, Remote Usability testing, Expert review, Automated expert review and A/B testing are the methods commonly used for Usability testing. However, there is no reliable system that integrates Brain Computer Interface (BCI) into the testing process with focus given towards user emotion analysis using electroencephalography (EEG) signals. This paper proposes a system that would be able to identify user emotions while they are conducting usability tests and the results would be able to increase the accuracy of the usability test. In the proposed system the results of the usability test would be displayed in real time on a dashboard and a summary report can be generated for distribution.</p>

scholarly journals A Proposed Web Based Real Time Brain Computer Interface (BCI) System for Usability Testing

2019 ◽  
Vol 15 (07) ◽  
pp. 111
Author(s):  
Hewawasam Puwakpitiyage Chamode Anjana ◽  
Paramesura Rao Vegnish Rao ◽  
Muhammad Azizi Muhammad Syahir Amali bin ◽  
Tee Wee Jing ◽  
Murugesan Raja Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Usability Testing ◽  
Computer Interface ◽  
Web Based ◽  
Expert Review ◽  
Usability Test ◽  
Usability Tests ◽  
Remote Usability Testing ◽  
Bci System

<strong>Hallway testing, Remote Usability testing, Expert review, Automated expert review and A/B testing are the methods commonly used for Usability testing. However, there is no reliable system that integrates Brain Computer Interface (BCI) into the testing process with focus given towards user emotion analysis using electroencephalography (EEG) signals. This paper proposes a system that would be able to identify user emotions while they are conducting usability tests and the results would be able to increase the accuracy of the usability test. In the proposed system the results of the usability test would be displayed in real time on a dashboard and a summary report can be generated for distribution. </strong>

scholarly journals Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Pablo Martinez ◽  
Hovagim Bakardjian ◽  
Andrzej Cichocki
Keyword(s):  
Real Time ◽  
Healthy Subjects ◽  
High Speed ◽  
High Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Optimal Frequency ◽  
Computer Screen ◽  
Near Future ◽  
Bci System

We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

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