Speed Control of a Wheelchair Prototype Driven by a DC Motor Through Real EEG Brain Signals

Abstract

For some disabled people, Electroencephalogram (EEG) signals are used to interpret brain thinking to drive machines by creating interface between the human brain and such machines. EEG signals are naturally varied due to human thinking process, and can be manipulated to drive a wheelchair based DC motors in real-time without any muscular efforts. In this paper, EEG signals are used to control DC motors using a Brain Computer Interface (BCI) that includes an EEG sensor headset to capture brain signals. The extracted EEG signals are considered as reference signals and transmitted to a microcontroller via Bluetooth. An intelligent wheelchair (IW) with an EEG sensors is connected to an Arduino, that drives two DC motors, to control movement references to the specific EEG signals. For the proposed IW based EEG, life cycle cost (LCC), over 5 year lifetime, is about 2674$ compared with a manufactured passive wheelchair, which its LCC is 3957$. The experimental tests suggest that the proposed design of IW is efficient and low cost as well as allowing disabled people to more easily control their wheelchairs and to lead independent lives.

Design And Fabrication of Temperature and Humidity Stabilizer on Low Voltage Distribution Panel with PLC-Based Fuzzy Method to Prevent Excessive Temperature and Humidity on The Panel

“…Motor Driver L298N is a dual motor driver module or has 2 outputs. So the motor driver type can control 2 motors at once up to 2A [30]. The L298N motor driver can also be connected with simple manual switches, TTL logic gates, relays, etc [30].…”

“…So the motor driver type can control 2 motors at once up to 2A [30]. The L298N motor driver can also be connected with simple manual switches, TTL logic gates, relays, etc [30]. This module is also equipped with a power LED indicator, an on-board +5V regulator, and a protection diode [31].…”

Eye-Gaze Controlled Wheelchair Based on Deep Learning

“…A growing body of research is dedicated to the application of artificial intelligence technologies to modify power wheelchairs to improve the quality of life of people with ALS. In the past few decades, researchers have carried out corresponding research on wheelchair motion control methods, including gesture control [1][2][3][4][5], voice control [6][7][8][9][10], eye-tracking control [11][12][13][14][15], and brain-computer interfaces [16][17][18][19][20][21][22]. These control methods can replace the rocker to complete the reading of the user's motion direction intention and realize the motion control of the wheelchair.…”