Brainwave Controlled Robot Using Bluetooth

2017

Paralysis is one amongst the major neural disorder that causes loss of motion of one or more muscles of the body, where in depending on the cause, it may affect a specific muscle group or region of the body or a larger area may be involved. In pursuit of rehabilitation, the eye can be regarded as one of the organs that can help a paralyzed person to communicate suitably. The Brain Signals of such patients can be used to help them communicate to others and also to perform various tasks by providing necessary infrastructure and training. This project describes the acquisition and analysis of Brain signals for operating a robot having a robotic arm mounted on top of it. The proposed method here uses a minimum number of electrodes for obtaining the brain signals using EEG Headsets available in the market and then control a robot based on the levels of these brain signals which can be varied by varying the states of mind. The EEG Headset detects the signals and generates a discrete value. This value is then sent over Bluetooth to a PC/ Laptop for further processing and plotting using MATLAB. After processing the actions to be performed are sent over ZIGBee to the ARM Microcontroller that controls the robot as well as the robotic arm mounted on the robot.

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.

Journal of Robotics and Control (JRC), 2021

The development of the world of robotics is inevitable with the rapid development of supporting science and technology. There are various types and classifications of robots, although the basic development is not much different. One type of robot that is in demand and the most widely developed is the wheeled robot. The robot component itself is generally divided into 3 parts, the first sensor, the second processor or component processor and actuator, in this study which behaves as an actuator is a wheel, while that behaves as a sensor, researchers utilize brainwave reader headsets from neurosky, and those that served as a processor component or processor using Arduino Uno R3. The neurosky headset works wirelessly using a Bluetooth connection, and the data sent is in the form of a brain wave power level (blink streght level). Before it can be translated into a telepathic brain command, this signal is first captured and processed using an android handset using an application that is built based on blynk IoT, then after that the command is sent to Arduino as a robot processing component that has previously been fitted with HC-06 bluetooth module hardware. to capture wireless broadcasts from an android device, only after that the signal is processed by Arduino becomes a command to move forward, backward, left, right wheeled robot by the L298N motor driver. The test results in an ideal environment showed an average system success of 85%, while testing in a non-ideal environment (with obstacles of space and distance) showed an average system success of 40% with each test carried out 10 times.

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.

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.

Brain-computer interface (BCI) system provides communication channel between human brain and external devices. The system processes and translates thought into control signals and thus enabling a user to navigate a robot from one place to another. In this context, we developed a system that enables a user to guide a robot by brain waves. The system consists of an Emotiv Epoc headset, a personal computer, and a mobile robot. The Emotiv Epoc headset attached to the head of the user and used to collect Electroencephalogram (EEG) signals. The headset picks up brain activities from 14 locations on the scalp and sends them to the computer for processing. Those brain activities can tell the system what a person is going to do in his virtual reality. Then, by using a novel application designed for this purpose, the cognitive suite supplied by Emotiv is responsible for generating the control actions needed to make the robot execute three different commands: turn right, turn left, and move forward. In this paper, hardware and software architectures were designed and implemented. Experimental results indicate that the robot can be successfully controlled in real-time based on the subject's physiological changes. Keywords: brain computer interface (BCI), electroencephalogram (EEG), emotiv epoc neuroheadset.

2020

The locomotive disabled people and elderly people cannot control the wheelchair manually. The key objective of this paper is to help the locomotive disabled and old people to easily manoeuvre without any social aid through a brainwave-controlled wheelchair. There are various types of wheelchair available in the market such as Voice controlled wheelchair, Joystick control wheelchair, Smart phone controlled wheelchair, Eye controlled wheelchair, Mechanical wheelchair. These wheelchairs hold certain limitations for e.g. if the user is dumb; user cannot access voice controlled wheelchair, etc. Brain-computer interface (BCI) is a new method used to interface between the human mind and a digital signal processor. An Electroencephalogram (EEG) based BCI is connected with an artificial reality system to control the movement and direction of a wheelchair. This paper proposes brainwave controlled wheelchair, which uses the captured EEG signals from the brain. This EEG signals are then passed ...

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.

2016 International Conference on Informatics and Computing (ICIC), 2016

We introduce the design and preliminary implementation of low-cost brain-computer interface (BCI) to enable movement of a rolling robot. This system will accept and execute basic commands that are generated from three brain condition normal, relax, or happy. The brain condition is determined by brainwaves known as alpha, beta, and gamma waves. The brainwaves are detected using Mindflex, connected with Arduino Uno, and sent data transmission to computer via USB connection. Algorithm is developed using Matlab to analyze the data and send three simple commands to a rolling robot through Wi-Fi connection. We conducted experiment for fifty times using 100 data for training and 50 data for testing, and obtained 62% of accuracy. This result shows that brainwave commands can be processed successfully for forward, turn left, and turn right movements. We have concluded that the system can be developed further to assist disabilities performing motions using their minds.

Advances in Human-Computer Interaction, 2013

Humans have traditionally interacted with computers or machines by using their hands to manipulate computer components. his kind of human-computer interaction (HCI), however, considerably limits the human's freedom to communicate with machines. Over the years, many attempts have been made to develop technologies that include other modalities used for communication, for example, speech or g e s t u r e s ,t om a k eH C Im o r ei n t u i t i v e .R e c e n ta d v a n c e si n cognitive neuroscience and neuroimaging technologies in particular have allowed for the establishment of direct communication between the human brain and machines. his ability is made possible through invasive and noninvasive sensors that can monitor physiological processes relected in brain waves, which are translated online into control signals for external devices or machines. Such brain-computer interfaces (BCIs) provide a direct communication method to convey brain messages to an external device independent from the brain's motor output. hey are oten directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. In BCIs, users explicitly manipulate their brain activity instead of using motor movements in order to produce brain waves that can be used to control computers or machines. he development of eicient BCIs and their implementation in hybrid systems that combine well-established methods in HCI and brain control will not only transform the way we perform everyday tasks, but also improve the quality of life for individuals with physical disabilities. his is particularly important for those who sufer from devastating neuromuscular injuries and neurodegenerative diseases which may lead to paralysis and the inability to communicate through speech or gesture. In a BCI system, brain activity is usually recorded using a noninvasive neuroimaging technology, such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), or nearinfrared spectroscopy (NIRS). In some cases, invasive technologies are used, such as electrocorticography (ECoG). In the majority of BCI systems, scalp EEG data are recorded, with the type of BCI system categorized based on the measure of brain activity used for BCI control. Each system has its own shortcomings and disadvantages. For instance, the information transfer rates of currently available noninvasive BCI systems are still very limited and do not allow for versatile control and interaction with assistive machines.