Offline EEG-based DC Motor Control for Wheelchair Application
Brain-computer interface (BCI) connects the brain of human with computer, where it allows people with physical disabilities to operate different electronic devices with the help of brain waves. The process can be performed without any involvement of human touch. This system will provide an easy use...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27519/ http://umpir.ump.edu.my/id/eprint/27519/1/Offline%20EEG-based%20DC%20Motor%20Control%20for%20Wheelchair1.pdf http://umpir.ump.edu.my/id/eprint/27519/2/Offline%20EEG-based%20DC%20Motor%20Control%20for%20Wheelchair.pdf |
| Summary: | Brain-computer interface (BCI) connects the brain of human with computer, where it allows people with physical disabilities to operate different electronic devices with the help of brain waves. The process can be performed without any involvement of human touch. This system will provide an easy use and operation of certain device by disabled people. The system is fit for people, who have no control over their normal muscular body to use the peripheral devices. In addition, due to the good feature of this technology namely, user friendly and low cost, it is getting more popularity recently. The application of BCI is very wide which cover medical and non-medical application. for instant, playing games, BCI speller, cursor control, social interactions by detecting emotions, robotic arm control, wheelchair control, home appliances control or smart phone operation using Electroencephalogram (EEG) signals are all applications of BCI technology. In this research, the possibility of DC motor control using single channel EEG headset has been investigated. The research aims to find the best EEG features and classifier where the output of the classifier can provide a correct device command to control DC motor movement. Here, EEG feature in terms of power spectral density has been extracted and classified using support vector machine (SVM) with the classification accuracy achieved at 92%. Then, the classified EEG
features had been translated into three devices command to control the direction of DC motor. The DC motor can be driven in three direction namely forward, right and left direction. Data collection from EEG headset and sending commands to dc motor, the entire process has been done wirelessly. The multi direction of DC motor will enhance the wheelchair application by disabled people. |
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