Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

William C Francis, C Umayal, G Kanimozhi

Abstract


The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI.  The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

Keywords


Electroencephalography; Brain- Computer Interface ; Brainwaves; Wheelchair control ; non-invasive EEG; EEG electrodes

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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