ELECTROENCEPHALOGRAPHY CONTROL WHEELCHAIR USING DEEP LEARNING

The vision of this project is to offer effective and efficient options to control the mobility of the wheelchair for people who suffer from paralysis from the neck down to people who have quadriplegia or paraplegia. The disabled community of the world with paraplegia and quadriplegia require technol

Project Title

ELECTROENCEPHALOGRAPHY CONTROL WHEELCHAIR USING DEEP LEARNING

Project Area of Specialization

NeuroTech

Project Summary

The vision of this project is to offer effective and efficient options to control the mobility of the wheelchair for people who suffer from paralysis from the neck down to people who have quadriplegia or paraplegia. The disabled community of the world with paraplegia and quadriplegia require technological assistance in the form of a wheelchair for inclusion not only in the societal aspect but the economical aspect of the region as well. This problem arises the need of specifically designed wheelchairs. This project focuses on that need by providing navigation control through BCI. These EEG signals help in the monitoring of the patient's health by showing the levels of stress, meditation, and attention from the brain. Brian control system is based on brain computer interface technology in which brain EEG signals of different frequency ranges are processed, filtered, and converted into a meaning full control signal using microcontroller, that are further mapped to drive the wheelchair. Neural brain activities are monitored through TGAM module with single channel dry electrode. BCI is real time communication between the human brain and the machine it provides quick response of the EEG signals, high mobility & quick setup.

Project Objectives

Our objectives are to use electroencephalographic signal human brain and convert them to use able commands. To do so firstly EEG signal filtration to get almost pure EEG signal without the distortion of any noise and secondly processing of these EEG signals to distinguish between different responses of human mind to get desire able response and these responses are Mapped in to meaning full commands and serially communicated to wheelchair hardware furthermore providing ease to care takers by creating joystick that can enable them to control the wheelchair.  Selection of motors and efficient control implementation with full control on speed of wheelchair. Another objective to use pulse width moderation control for the motors smooth running. Another benefit of using EEG control is that the EEG signal are real time monitor of brain through which we can real time monitor the health of patient.

Project Implementation Method

The designed wheelchair is the production of best hardware and software integration. The system is powered through two batteries of 12V and a toggle switch is used to select if we want to use the joystick or BCI control system using EEG signal to control the wheelchair. The BCI control system is being used extensively all around the globe due to the quick response of the EEG signals, high mobility & quick setup.

Major sections of the project:

• Electroencephalographic control
• Joystick control
• Motor control
• Hardware

EEG driven control operation requires certain steps of processing of a microvolt signal with the help of dry electrode to capture this signal. The signals contain vast amount of disruption random wave to filter the EEG signals we are applying various filters. At first, we apply the low cancelation process to filter raw EEG signal then distribute this raw signal in five different frequency bands. These bands give five different signals to distinguish between different responses of the brain to map and make certain command against these responses. The five bands of frequencies are Alpha signals (range between 8-13Hz), Beta signals (range between 14-30Hz), Delta signals (range between 0.1-3.5Hz), Theta signals (range between 4-7.5Hz) and Gamma signals (go above 30Hz) [17]. The different responses of the brain generate different EEG signals which can be processed to acquire specific signals to perform the desired tasks. If BCI control option is selected, then the system reads data through serial communication between TGAM chip and Raspberry pi and the wheelchair is driven according to the commands from EEG signals to Raspberry pi . On the other hand, if EEG commands are not available, the system switches to control through the joystick. If the option of joystick is selected from the toggle switch then the x or y input is received from the user through the joystick and, the system interprets the commands and runs the motors, respectively.

Benefits of the Project

Our project is to create a BCI control system using EEG signals to control the movement of the patient’s wheelchair which helps the user to control the movement of the wheelchair while monitoring his/her health, providing freedom of motion to paralyzed people or those who are suffering from spinal cord injuries and unable to interact with the surrounding and it can also increase the recovering period of the neural disorder by exercising the unused nerves again monitoring patient’s health status and mind condition to provide extra layer of safety.

Technical Details of Final Deliverable

Final deliverables:

No#

1

2

3

4

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Medical

Other Industries

Manufacturing , Others , Health

Core Technology

NeuroTech

Other Technologies

Artificial Intelligence(AI), Wearables and Implantables, Others

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com