A low cost Human Computer Interface for Disabled People based on Eye Blink detection using Brain Signal

Rahman, Lamiya; Adan, Jannatul; Billah, Quazi Mutasim; Islam, Kamrul; Kamal, Abu Hena Mustafa; Hossain, Sazzad; Shahnaz, Celia; Subhana, Arik

doi:10.1109/tencon.2019.8929452

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…However, we looked at the bright side of eye blink artifacts and put them to use. They are used as an input [4] from the paralyzed patients to control their wheelchair. The few advantages of using eye blink artifacts as the control element are as follows:…”

Section: Eye Blink Artifactmentioning

confidence: 99%

“…Previous works on wheelchair control based on eye blink patterns have been limited to very few control elements due to the limited number of patterns that could be created with eye blinks. For example, in [12], every time the patient blinks their eyes twice rapidly, the wheelchair will turn on or off. This accounted for bidirectional control of the wheelchair but in a real case scenario, four or more control patterns are required.…”

Section: Gui Developmentmentioning

confidence: 99%

“…In this research work, the feature that needs to be extracted from the EEG signal is called Eye blink artifact. Each time the subject blinks their eye, it is extracted [4] and classified as a command for movement of the wheelchair in a specific direction. This completes the classification step.…”

Section: Introductionmentioning

confidence: 99%

“…Many people suffer from paralysis as a result of damage in their nervous system, especially the spinal cord, or due to Amyotrophic Lateral Sclerosis (ALS). With the loss of motor functions, these individuals are forced to be dependent on others for fulfilling their daily needs and care [4,12,13]. Unfortunately, this leads to a lower quality of life for them.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Francis

Umayal

Kanimozhi

2021

IJEEI

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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.

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Section: Eye Blink Artifactmentioning

confidence: 99%

Section: Gui Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Francis

Umayal

Kanimozhi

2021

IJEEI

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“…The authors in [18], [19] examines eye blinks of a test subject as well as detecting their brain signals to behave as a conduit among a set of options indicated in the display where the user can select and command electrical appliances, wheelchair, and even a computer with no reliance on others. In another effort, [20] proposed an EEG analysis tools for emotion characterization using mobile robot for autism patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

F.¹,

Miskon²,

Hamzah³

et al. 2021

IJETAE

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Disabled people are usually unable to interact with their surroundings efficiently, and performing tasks like switching an appliance on or off can be troublesome if the user is bedridden, for example. This article discusses an electrical appliance switching controller using a wireless EEG headset that is aimed to aid elderly people and the disabled. The system comprises of a MindLink EEG headset that is Bluetooth-connected to an Arduino microcontroller board. The system permits the user to separately switch on and off the 4 electrical devices connected to the power socket. The EEG signal is obtained to investigate the brain activity throughout the experiments done. Based on the brain wave signals read, attention and meditation are determined to be the most suitable for this project and is used to trigger the relay switching of the power socket. It is found that the response time to trigger the switching is slow as some users require practice or training to control their brain wave signals effectively. The work performed provides a rudimentary insight of a BCI system functionalities and presents a brainwave-controlled hardware switching for the bedridden or disabled patients.

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EEG Based Brain State Classification Technique Using Support Vector Machine -A Design Approach

Agrawal

Bajaj

2020

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)

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A low cost Human Computer Interface for Disabled People based on Eye Blink detection using Brain Signal

Cited by 6 publications

References 7 publications

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

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

Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset

EEG Based Brain State Classification Technique Using Support Vector Machine -A Design Approach

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