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

Abstract

The human brain is considered 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: 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. An embedded Bluetooth module acquires and transfers the signals 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 realtime 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 wheelchair attained a command detection and execution accuracy of 96.4% which is an improvement from the existing systems. 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.

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

“…So, it is possible that everyone has a different threshold and width because everyone's blinking speed is different. In addition, as suggested from the previous study, threshold parameters can be determined in the presence of positive and negative blink strength thresholds [31]. Here, the positive threshold is obtained from the positive max peak value and the negative threshold is obtained from the negative min peak value.…”

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

“…In Fall et al (2015), Thorp et al (2015), Fall et al (2018), Penaloza and Nishio (2018), Ansari et al (2019), andFrancis et al (2021), body-machine interface (BMI) was developed, taking advantage of the user's residual motion. They extract the user input from the kinematics of both neck and shoulder to then produce a proportionally controlled signal and drive the power wheelchair.…”