Brainwave Controlled Robotic Arm

The latest trend within the brain wave technology has been mentioned in this paper, and the way the brain Wave controlled mechanism works based on Brain– Computer interfaces (BCI) conjointly discussed. BCIs are systems that can bypass standard channels of communication (i.e., muscles and thoughts) to produce direct communication and management between the human brain and physical devices by translating totally different patterns of brain activity into commands in real time. With these commands a mobile robot will be controlled. The intention of the project work is to develop a mechanism that can assist the disabled individuals in their everyday life to do some work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of several interconnected neurons.

To create a robotic arm that functions accordingly to the human brain signals without any medical surgeries.

This project discusses about a brain controlled robot based on Brain Computer Interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mobile robot can be controlled. The intention of the project work is to develop a robot that can assist the disabled people in their daily life to do some work independent of others. Here, we analyze the brain wave signals. Human brain consists of millions of interconnected neurons. The pattern of interaction between these neurons is represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. Muscle contraction also generates unique electrical signals. All these electrical waves will be sensed by the brain wave sensor and converts the data into packets and transmits through Bluetooth medium. The brain wave raw data is sent to the computer and it will extract and process the signal using MATLAB platform. Then the control commands will be transmitted to the robot module to process. With this entire system, we can move a robot based on the human thoughts and it can be turned by blink muscle contraction.

IOP Conference Series: Materials Science and Engineering, 2019

The purpose of this study is to discuss the brain wave system that can move the prosthetic arm based on brain wave activity. The sensor used to detect EEG brainwave activity uses a mobile mind wave sensor. Movement and detection of brainwave signals is carried out in the Lab VIEW application program. Plan this robot to make movements based on brain wave activity, utilizing blinks and attention. This research method through a process carried out to control the prosthetic arm. Where there are 2 modes, the first mode for the selection of movements with a blink of an eye, and the second mode of attention to move the fake arm. Based on research results Prosthetic arms can make movements that are designed for extension, flexion, supination or pronation and increase or depression. The prosthetic arm can make movements based on the subject's commands by utilizing brain wave activity. With a speed response time of 9.54 seconds to do all the moves. In addition to the advantages of this artificial arm, it can accommodate objects with a diameter of 2.2 cm to 6 cm. With an average percentage success of 6 experiments conducted by 86.67%.

Reegan R

The aim of the project is to enhance the interactivity for controlling a mind-controlled Robotic ARM using Brain-Computer Interface (BCI) technology in an open-source environment by adding a virtual world input, enabling users to interact with the real world and making the entire process more user-friendly. The project proposes a new solution for human-robot interaction by incorporating a smart chip implanted in the radial nerve of the human brain, replacing the current EEG wearable helmet. The chip will contain EEG technology and a Bluetooth extension, allowing for real-time, non-invasive control of the robotic arm through EEG signals captured by the BCI technology. The Bluetooth extension will provide wireless communication capabilities, enabling physically challenged individuals to perform day-to-day activities with greater ease and independence. The use of the smart chip in the radial nerve provides a more natural and intuitive method of control compared to the current EEG wearable helmet, as the implantation of the chip allows for real-time, non-invasive control of the robotic arm through EEG signals captured by the BCI technology.

This paper describes about a brain controlled robot based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mobile robot can be controlled. Here the robot is self-controlled with the ultrasonic sensor. The intention of the project work is to develop a robot that can assist the disabled people in their daily life to do some work independent on others. Here, we are analysing the brain wave signals. Human brain consists of millions of interconnected neurons.

International Journal of Computer Applications, 2014

This paper describes the Mind Controlled Robot based on Brain Computer Interface (BCI) using LabVIEW to analysis the brain waves. BCIs are systems that may bypass typical channels of communication (i.e., muscles and thoughts) to supply direct communication and management between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mobile robot can be controlled. The intention of the project work is to develop a mechanism that may assist the disabled folks in their everyday life to do some work freelance on others. Here, they tend to are analyzing the brain wave signals. Human brain consists of innumerable interconnected neurons. The patterns of interaction between these neurons are delineating as thoughts and emotional states. In step with the human thoughts, this pattern are going to be dynamical that successively manufacture totally different electrical waves [1].

Arxiv, 2022

This paper presents Open-source software and a developed shield board for the Raspberry Pi family of single-board computers that can be used to read EEG signals. We have described the mechanism for reading EEG signals and decomposing them into a Fourier series and provided examples of controlling LEDs and a toy robot by blinking. Finally, we discussed the prospects of the brain-computer interface for the near future and considered various methods for controlling external mechanical objects using real-time EEG signals.

Kalpa Publications in Engineering

This research paper presents to develop a bio-signal acquisition system and rehabilitation technique based on “Cognitive Science application of robot controlled by brain signal”. We are trying to Developing a data acquisition system for acquiring EEG signals from Brain sense head band and also designing new algorithm for detecting attention and meditation wave and implementing on Robotics platform By using Embedded core.

Electrooculography is a technique for measuring the resting potential of the retina. The resulting signal is called the electrooculogramThe bio-potential signal also is one of the examples of human-machine interface using of nonverbal information such as electrooculography (EOG), electromyography (EMG), and electroencephalography (EEG) signals. The EOG and EMG signals are physiological changes; but here we are focusing the mainly on EOG signals for the human-machine interface. This papert has investigated that different EOG signals obtained from four different places around eye; (right, left, up, and down) have led to different level of distance and rotation of wheelchair. Those four signals are correspond to different levels of right and left steer, forward and backward motion. There are many research that have concentrated in making use of the eye movement signals for tetraplegia. Despite of all the complexity that arises when analyzing the eye movement signals. In this case the constraints are made such that the eye movement is assumes to be very limited to; (straight-to-up, straight-todown, straight-to-right and straight-to-left). The issue of other eye movement patterns.

MATEC Web of Conferences

In this paper, we analyze the principles of brain computer interface that convert the electrical activity of the human brain into commands for a computer of any embedded device. For this reason, we present the existing devices and applications from the area of braincomputer interfaces with advantages and disadvantages. Further, we propose a solution for brain control of a robotic arm. We develop the model and simulate the entire system functioning, both the robotic arm control and the brain signals processing. The final purpose of our research is to achieve a brain-computer system that controls a robotic arm that can replace a human arm.

There are number of physically handicapped people. Some of them are using different technologies to move around. The proposed work implements a robot which is controlled using human brain attention.Here brain signals analyzes using electrode sensor that monitors the eye blinks and attention level. Brain wave sensor that detects these EEG signals is transmitting through Bluetooth medium. Level analyzer unit (LAU) i.e. computer system will receive the raw signals and process using MATLAB platform. According to human attention level robot will move. ARM controller is used to design robot.

Electrooculography is a technique for measuring the resting potential of the retina. The resulting signal is called the electrooculogramThe bio-potential signal also is one of the examples of human–machine interface using of nonverbal information such as electrooculography (EOG), electromyography (EMG), and electroencephalography (EEG) signals. The EOG and EMG signals are physiological changes; but here we are focusing the mainly on EOG signals for the human–machine interface. This papert has investigated that different EOG signals obtained from four different places around eye; (right, left, up, and down) have led to different level of distance and rotation of wheelchair. Those four signals are correspond to different levels of right and left steer, forward and backward motion. There are many research that have concentrated in making use of the eye movement signals for tetraplegia. Despite of all the complexity that arises when analyzing the eye movement signals. In this case the constraints are made such that the eye movement is assumes to be very limited to; (straight-to-up, straight-to-down, straight-to-right and straight-to-left). The issue of other eye movement patterns. Keywords—Brain computer interface, Electroculogram, Electrodes, Robotic Prototype Model

2020

1-3Student, Dept. of Computer Engineering, Terna Engineering College, Navi Mumbai, India. 4Professor, Dept. of Computer Engineering, Terna Engineering College, Navi Mumbai, India. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract The field of prosthetics has showed a significant improvement over last few years, due to advancement in technologies. However, they have certain problems either with being really expensive, does not provide full motor functions, may require surgical approach or does not look like an arm. This project describes how the Brain waves can be used to control a prosthetic arm using Brain Computer Interface (BCI). The BCI system consist of Electroencephalogram (EEG) sensors placed on the headset to capture the brain waves, which will be extracted using Thinkgear library in MATLAB. The Brain signal act as command signals and transmitted to microcontroller. This comma...

A brain machine interface (BMI) facilitates the control of machines through the analysis and classification of signals directly from the human brain.