Wheelchair controlled by human brainwave using brain-computer interface system for paralyzed patient
Brain-Computer Interface (BCI) is a direct communication pathway between a human and external device. Integrated wheelchair controlled with human brainwave using a BCI system was designed and studied to help people with disabilities, especially for people who suffer from motor disorders such as peri...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
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| Online Access: | http://eprints.uthm.edu.my/1108/1/24p%20NORASYIMAH%20BINTI%20SAHAT.pdf http://eprints.uthm.edu.my/1108/2/NORASYIMAH%20BINTI%20SAHAT%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1108/3/NORASYIMAH%20BINTI%20SAHAT%20WATERMARK.pdf |
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| Summary: | Brain-Computer Interface (BCI) is a direct communication pathway between a human and external device. Integrated wheelchair controlled with human brainwave using a BCI system was designed and studied to help people with disabilities, especially for people who suffer from motor disorders such as peripheral nerves and muscles. The invention aims to develop an integrated wheelchair which can be controlled by a paralyzed person using only a single electrode. In this research, the efficiency of the brainwave integrated wheelchair has been improved using human attention value, blink detection and eyebrow movement of the user to control the wheelchair. An encephalography (EEG) device called Mindwave Mobile Plus (MW+) has been employed to obtain attention value for the wheelchair movement, eye blink to change the mode of the wheelchair to move forward (F), to the right (R), backward (B) and to the left (L). Eyebrow movement was used to stop the wheelchair when using human brainwave as the signal quality value of 26 or 51 is produced. Analysis on the human attention value in different gender and age category also has been done. Male is easier to focus compared to the female. Teenagers have the highest attention value followed by the children while the adults have the lowest attention value among all age categories studied. The EEG of the human were analyzed by using Arduino Integrated Development Environment (IDE) software. The development of the integrated wheelchair is improved by using human’s attention value, blink detection and eyebrow movement and the threshold value of the attention level was set according to the gender and age category of the user. From the results and analysis, the threshold value for male children is 60, male teenager (70), male adult (40) while for the female children is 50, female teenager (50) and female adult (30). |
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