Controlled Wheelchair Based on Brain Computer Interface Using Neurosky Mindwave Mobile 2.

Stroke diminishes someone’s quality of life since post-stroke symptoms can make some limbs do not function normally -in most cases, feet-thus the stroke patients’ mobility will be limited. However, the brain wave of stroke patients was observed as normal, thus in this study, the normal brainwaves were utilized to rehabilitate post-stroke patient and were expected to help their mobility through wheelchairs with brain waves control so that post-stroke patients can improve their quality of life. Brain Computer Interfaces (BCI) is a technology that allows taking action on a computer based on brain waves. Brain waves are recorded by electroencephalography so they can be processed by a computer. There have been many studies using BCI including analyzing brain waves in humans, many things that can be utilized using BCI make a lot of researchers use them to make smart wheelchairs that use brain control. and developments continue to be made to create the most optimal system. In this project we use one of the topics that is still being developed, that is Motor Imagery, where we record and analyze the brain waves while imagining motor activities such as moving hands, walking, running and so on. This record will serve as data reference to trigger the process on the computer to move the actuator. The purpose of this project is to control the wheelchair based on motor movements obtained by BCI using the Neurosky Mindwave Mobile 2 headset. This headset has one electrode where the signals from one electrode are analyzed by concentration and meditation values from the case of imagery motors, which in this project are more portable than using conventional EEG data acquisition devices that are not portable and use many channels. This headset is able to record data wirelessly via Bluetooth to a PC (Personal Computer), so the obtained signal can be processed and classified into five movement classes using the Matlab GUI, where the classes are default/motionless, move forward, move backward, turn right and turn left. The method used for the wheelchair was replacement of the default joystick in the electric wheelchair with an self-made controller module which was based on brain waves signals obtained from the headset will be processed and classified by Matlab GUI and then forwarded to Arduino Uno to control the motor in the wheelchair. The average success rates of the five classes from five trials were: the first class with a success rate of 82.22 %, the second class with a success rate of 70 %, the third class with a success rate of 73.33 %, the fourth class with a success rate of 46.67 % and the fifth class with a success rate of 17.78 %. The results of this study indicate that Neurosky Mindwave mobile 2 headset can be a possible choice for this project. [ABSTRACT FROM AUTHOR]