MIND CONTROLLED ROBOTIC ARM - IRJMETS- Journal ...• Arduino is programmed for set controlled commands to Robotic Arm and also for Bluetooth module interface. • Both the Brain sense

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science

Volume:02/Issue:03/March-2020 www.irjmets.com

www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science

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MIND CONTROLLED ROBOTIC ARM

Saravana Pandian.M*1

, Karthikeyan.S*2

, Ijaz Mohammed.S*3

, Kiran.K*4

*1Professor, Department of ECE, Jeppiaar SRR Engineering College, Padur, Chennai, India.

*2,3,4 B.E Final year, Department of ECE, Jeppiaar SRR Engineering College, Padur, Chennai, India.

ABSTRACT

This project attempts to implement an Arduino robotic Arm to simulate a brainwave-controlled. The robot

should be ready to move freely anywhere under the control of the user and it’s not required to predefine any

map or path. The controlled command is obtained from brain sense. BCI is going to be developed by sending the

EEG signal to the Arduino UNO and control the movement of the robot. This project is implemented by using

the attention blinking as the robot controlling method because the eye blinking will provide a required pulse for

EEG signal. By the application of neural network, is useful to classify the blinking signal and thus the noise.

The user can send the command to regulate the robot by blinking twice during a short period.

KEYWORDS: Brain-Computer Interface, Electroencephalogram, Neural network, Eye Blink, Neuro-sky

sensor, Robotic arm.

I. INTRODUCTION

The Systems capable of understanding the various facets of human communication and interaction with

computers are among trends in Human-Computer Interfaces (HCI). An HCI which is formed on the guiding

principle (GP): “think and make it happen with none any physical effort” is named a brain-computer interface

(BCI).By this project, the robot will be evaluated by driving in different places to test whether it can able to

follow the expected path, to avoid the obstacles, and to stop on a specific position.

II. SIGNAL FREQUENCY

The main four frequency of the human EEG waves are:

Beta:

Beta activity is a "fast" activity. It features a frequency of 14 and greater Hz. It is seen on both sides in

symmetrical distribution and also the most evident frontal. Beta is accentuated by sedative-hypnotic drugs

specifies the benzodiazepines and the barbiturates. Beta may be reduced in areas of cortical damage. It’s

considered a standard rhythm.




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Alpha:

Alpha has a frequency range between 7.5 and 13 Hz. It usually is best seen within the posterior regions of the

head on each side, being higher in amplitude on the dominant side. Alpha appears when closing the eyes and

relaxing, and disappears when opening the eyes or alerting by any mechanism of thinking, calculating. It is the

main rhythm seen in normal relaxed adults. It is present during most of life especially after the age of the

thirteenth year.

Theta:

Theta Features a frequency of 3.5 to 7.5 Hz and is classified as "slow" activity. It is perfectly normal in children

under the age of 13 years and sleep but abnormal in awake teenagers. Theta is visible as a manifestation of focal

subcortical lesions and it mostly attains at children.

Delta:

Delta features a frequency range of 3 Hz or below. It tends to be high in amplitude and produces slowest waves.

It is normal because of the dominant rhythm in infants up to at least one year and in stages 3 and 4 of sleep. It is

usually the most prominent frontally in teenage (Frontal Intermittent Rhythmic Delta) and the posteriorly in

children (OIRDA - Occipital Intermittent Rhythmic Delta).

III. HARDWARE REQUIREMENTS

Mind wave Mobile or Brain-sense

Robotic Arm

Arduino Board

HC-05 Bluetooth Module

USB Cable for Arduino

Bluetooth dongle, if the pc doesn’t have inbuild Bluetooth capabilities

Batteries for Robotic-Arm or External Power supply ( 12 v )

1) Brain Sensor:

Technical specification:

• Module – TGAM 1

• Lead Electrodes – Main Electrode & Ear clip electrode(Ground)

• Type of Electrode – Non-Invasive | Dry

• Battery – 3 AAA Battery

• Stand up Time – 6 Hours run time

• Connectivity – Bluetooth v2.1 class.

• Compact ability: Windows XP | Android | Raspberry Pi | Arduino.

• Measure – Blinking | Meditation | Attention | EEG Bands (Alpha, Beta, Gamma, Delta & Theta) | Raw

EEG with Eyeblink




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Fig-1: Brain Sensor

2) Bluetooth Sensor:

• Two Bluetooth modems are connected.

• One is connected to the Robotic arm

• And another one is hooked up with the brainwave sensor

Fig-2: Bluetooth Sensor

3) Arduino Uno:

• Arduino Uno is an open-source microcontroller board based on the Microchip ATmega328P

microcontroller and developed by Arduino.cc.

• The board is build up with the set of digital pin and analog input/output pins that may be interfaced to

varied expansion boards and other circuits.

Fig-3: Arduino Uno




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4) Yellow Gearbox Motor:

The 6V yellow gearbox is a major source for robotic-Arm. With plastic construction and colored in

bright yellow.

The arm consists of Four gearbox motor

The gripper and arm can be mounted on either side and the Gearmotor works well between 4V to 7V

(recommended 6 Volts).

Fig-4: Yellow Gearbox Motor

Robotic Arm:

Robotic-arm consists of four gearbox motors used to move the fingers of the arms.

One motor is connected with Arm finger and another two motor connected with arm up and down

movement.

And the last motor is connected for robot rotation90’ degree (Clockwise, Anticlockwise)

The Programmable interface computer microcontroller will help to control the servo motors.

The robotic fingers are controlled by attention and meditation levels.

Fig-5: Robotic Arm




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Software Required:

Arduino Ide

Matlab Simulink

IV. BLOCK DIAGRAM

EEG SIGNAL MEDITATION LEVEL BLINK SIGNAL

BRAIN SENSOR WITH BLUETOOTH

CONNECTION

Fig-6: Block Diagram

V. WORKING AND ANALYSIS

• Arduino is programmed for set controlled commands to Robotic Arm and also for Bluetooth module

interface.

• Both the Brain sense and the Robotic Arm ( via HC-05 ) get paired to the laptop.

• The Matlab acquired the Raw EEG from our Brain via Brain sense and detected blink as the command

for Robotic arm.

• Matlab GUI shows the Graphical representation of our Brain waves and therefore the blink pattern.

MATLAB AND SIMULINK ROBOTIC ARM

ARDUINO WITH BLUETOOTH

ROBOTIC ARM




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• Initially, the are six commands are assigned to the robotic Arm, depends upon the eye blink the Matlab

pass the command to the robotic arm

Fig-7: Neuroview of strong eye blink(Delta)

Fig-8: Matlab of Simulink no Eyeblink (Beta)




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Fig-9: Matlab of Simulink with moderate Eyeblink(Theta)

VI. CONCLUSION

This brain-controlled mobile robot provides higher safety because they are used to transport paralyzed people.

This is often a major difference between brain-controlled mobile robots and other brain-controlled devices.

During this paper, the BCI system has been developed to address these challenges. A Brain-computer interface

is a system that can bypass conventional channels of communication. These mobile robots having applications

are Automotive Applications, Industrial Application, Home applications, Monitoring device applications,

Remote control applications. The mobile robot doesn’t need any additional robot intelligence. Cost is a smaller

amount also computational complex city is low. Having various applications such as Industrial Application

Home applications Automotive Applications, Monitoring, device applications, Remote applications.

VII. REFERENCES

[1] “A Brain-Computer Interface using electrocorticographic signals in Humans”, Eric C Leuthardt, Gerwin Schalk,

Jonathan R Wolpaw, Jeffrey G Ojemann & Daniel W Moran IOP Publishing Ltd,2004

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Biomedical Engineering and Sciences, pp 443-449, 2010.

[3] Narendra Bawane, “Brain-computer interface for hands-free computer accessibility”, International journal of

engineering sciences and Research, pp 556-561, August 2015

[4] Jinyi Long, Yuanqing Li, Hongtao Wang, Tianyou Yu, Jiahui Pan, and Feng Li, “A Hybrid Brain-Computer Interface

to Control the Direction and Speed of a Simulated or Real Wheelchair”, SEPTEMBER 2012

[5] Chiemla Onunka, Glen Bright, “Robotics and the brain-computer Interface System: Critical Review for Manufacturing

Application”.

[6] Brice Rebsamen, Cuntai Guan, “A Brain Controlled Wheelchair to Navigate in Familiar Environments”, IEEE

TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 18, NO. 6,

DECEMBER 2010




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[7] Rohan Hundia, “Brain-Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves”,

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 01,

JANUARY 2015.

[8] Yuanqing Li Jiahui Pan, Fei Wang, and Zhuliang Yu, “A Hybrid BCI System Combining P300 and SSVEP and Its

Application to Wheelchair Control”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO.

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[9] H.T. Nguyen, “Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control”, Proceedings of the 29th

Annual International Conference of the IEEE EMBSCité Internationale, Lyon, France August 2326, 2007

[10] Paola Belluom, Maide Bucolo, Luigi Fortuna, Mattia Frasca “Robot Control through Brain-Computer Interface for

Pattern Generation”, University of Catania, Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Viale A.

Doria 6, 95125 Catania, Italy,[email protected].

[11] Francesco Carrino, Joel Dumoulin, Elena Mugellini, Omar Abou Khaled1, Rolf Ingold, “A self-paced BCI system to

control an electric wheelchair: evaluation of a commercial, low-cost E E G device”,1 University of Applied Sciences

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[12] Gabriel Pires, Miguel Castelo-Branco and Urbano Nunes, “Visual P300-based BCI to steer a Wheelchair: a Bayesian

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[13] Dandan Huang, “Electroencephalography (EEG)-Based Brain-Computer Interface (BCI): A 2-D Virtual Wheelchair

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[14] “Brain-Computer Interfaces & Human-Computer Interaction”, Desney Tan & Anton Nijholt, Springer-Verlag London

Limited, 2010

[15] Christian J Bell, Pradeep Shenoy, Rawichote Chalodhorn and Rajesh P N Rao, “Control of a humanoid robot by a

noninvasive brain-computer interface in humans”, DOI:10.1088/1741-2560/5/2/012

[16] Jzau-Sheng Linl, Kuo-Chi Chen, and Win-Ching Yang, “EEG and Eye-Blinking signals through a Brain-Computer

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[18] Siliveru Ramesh, M.Gopi Krishna, Madhu Nakirekanti, “Brain-Computer Interface System for Mind-Controlled Robot

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Documents

MIND CONTROLLED ROBOTIC ARM - IRJMETS- Journal ...• Arduino is programmed for set controlled commands to Robotic Arm and also for Bluetooth module interface. • Both the Brain sense