Real Time Bci System Design To Control Arduino Based Speed Controllable Robot Using Eeg

This book discusses the basic requirements and constraints in building a brain–computer interaction system. These include the technical requirements for building the signal processing module and the acquisition module. The major aspects to be considered when designing a signal acquisition module for a brain–computer interaction system are the human brain, types and applications of brain–computer systems, and the basics of EEG (electroencephalogram) recording. The book also compares the algorithms that have been and that can be used to design the signal processing module of brain–computer interfaces, and describes the various EEG-acquisition devices available and compares their features and inadequacies. Further, it examines in detail the use of Emotiv EPOC (an EEG acquisition module developed by Emotiv) to build a complete brain–computer interaction system for driving robots using a neural network classification module.

The book contains selected research papers presented at the 2nd International Conference on Cyber-Physical Systems and Control (CPS&C’2021) which was held from 29 June to 2 July 2021 in St. Petersburg, Russia. The CPS&C’2021 Conference continues the series of international conferences that began in 2019 when the first International Conference on Cyber-Physical Systems and Control (CPS&C’2019) took place. Cyber-physical systems (CPSs) considered a modern and rapidly emerging generation of systems with integrated wide computational, information processing, and physical capabilities that can interact with humans through many new modalities and application areas of implementation. The book covers the latest advances, developments and achievements in new theories, algorithms, models, and applications of prospective problems associated with CPSs with an emphasis on control theory and related areas. The multidisciplinary fundamental scientific and engineering principles that underpin the integration of cyber and physical elements across all application areas are discussed in the book chapters. The materials of the book may be of interest to scientists and engineers working in the field of cyber-physical systems, systems analysis, control systems, computer technologies, and similar fields.

​This book constitutes the refereed proceedings of the 51st International Conference on Software Technology: Methods and Tools, TOOLS 2019, held in Innopolis, Russia, in October 2019.The 19 revised full papers and 13 short papers presented in this book were carefully reviewed and selected from 62 submissions. The papers discuss all aspects of software engineering and programming languages; machine learning; internet of things; security computer architectures and robotics; and projects.

Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG. Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed. An innovative reference on the brain-computer interface (BCI) and its utility in computational neuroscience and assistive robotics Written for mature and early stage researchers, postgraduate and doctoral students, and computational neuroscientists, this book is a novel guide to the fundamentals of quantum mechanics for BCI Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches A detailed introduction as well as an in-depth analysis of challenges and issues in developing practical brain-computer interfaces.

This text shows you how to build your own mind controlled robot. You learn to measure attention level with a NeuroSky headband and send this information into Arduino. You will also build a line-avoiding system into the bot. And, of course, you will build the chassis of your robot from scratch.

Due to the hands-free, non-invasive nature of electroencephalography (EEG) based control, research into brain-computer interface (BCI) systems has been a topic of interest in robotics applications. BCI systems have been studied in several applications including designing simple prosthesis, wheelchairs and virtual navigation, but its scope has often been constrained by several limiting factors. These factors include the need for lengthy training per each specific action desired, poor accuracy when dealing with multiple potential outputs and differences in brain signal behavior for each participant that make finding patterns that work for all individual test subjects a challenge. This research will focus on a method of controlling a robotic arm and dexterous hand system using a combination of BCI and machine learning to quickly train a model to recognize patterns from raw EEG data from a specific individual. This model will be tailored to that individual, allowing the subject to send a high-level input to initiate an adaptive command. The high-level adaptive command considers not only a broad intention of a desired action through EEG signals, but also sensor inputs and other user inputs to perform a desired action effectively. Research will be presented on a system wide implementation of a prototype of this design. The proposed brain-controlled robot is comprised of several major subsystems including the high level BCI input, a 4-degree of freedom (DOF) robot arm system with microcontroller, a 3-wheel omnidirectional mobile platform, a 9-DOF Brunel robot hand, and a MATLAB interface with an interactive GUI. The system receives inputs from an Xbox Kinect color and depth camera and respective microcontrollers that communicate with each other through serial ports, Bluetooth, and wired connections and with the environment through a force sensor, a Kinect depth sensor, and inputs from a MATLAB GUI and Xbox controller. This thesis research demonstrates the development of this multi degree of freedom integrated mobile robotic arm and gripper system that uses EEG data, Kinect image and depth inputs, and a force sensor to successfully control its operation after being trained using one machine learning session. A case study was performed where a subject was asked to record at least 25 sessions of each BCI command. 25% of the data from each test set was set aside for testing purposes. For a total of four different cases, an accuracy of 80% was reached whereas for five different cases, an accuracy of 76% was obtained. Motion of the robotic arm was simulated in MATLAB and successfully replicated in the robot prototype for grabbing different sized objects.

Current research on Brain-Computer Interface (BCI) controllers has expanded the opportunities of robotic applications within the biomechanical field. With the implementation of a real-time BCI controller, researchers have developed smart prosthetics, semi-autonomous wheelchairs, and collaborative robots for human interactions, allowing patients with neuromuscular disabilities the freedom to interact with the world. These advances have been made possible through the ease of non-invasive procedures for recording and processing electroencephalography (EEG) signals from the human scalp. However, EEG based BCI controllers are limited in their ability to accurately process real-time signals and convert them into input for a system. This research focuses on the development of a hybrid-BCI controller for a semi-autonomous three-wheeled omnidirectional robot capable of processing accurate real-time commands. EEG scans are recorded utilizing a fourteen-electrode channel cap provided by Easycap utilizing modified Emotiv Epoc hardware. Signals are recorded and processed by a program called OpenViBE in which users respond to different stimulus events. A MATLAB plugin, called BCILAB, is used to clean and process the data. This data is used to train the hybrid-BCI controller to be capable of differentiating between hand and foot motor imagery (MI) as well as jaw electromyography (EMG) signals. Once identified, the controller converts the signal into input commands of {forward, backward, left, right, rotate, stop}, which are published over LabStreamingLayer (LSL) to the robot. To date, omnidirectional mobile robots are popularly employed for their holonomic abilities, meaning they have three degrees of freedom (DoF) and are capable of traversing through its environment in any orientation. As such, a holonomic robot is proposed. The system is equipped with the Intel RealSense Depth Camera D435, as well as Lidar sensors to build a full map of the robot's surroundings. Robot operations are completed on the NVIDIA Jetson Xavier which runs the Robot Operating System (ROS). ROS manages all aspects of robot operations, called nodes. This includes receiving and translating BCI inputs, reading all sensor data, computing a trajectory and navigating the robot along the trajectory. Prototyping and developmental work was performed by creating a model of the robot in the Unified Robot Description Format (URDF) which can be run in Gazebo, a simulation software with a realistic physics model. The design of the system controller was tested in this simulated environment for both path planning and obstacle avoidance as well as receiving inputs from the BCI controller. The robot was able complete testing tasks and achieve goals with less than 10% error on average, often experiencing no more than 2% error when considering built in tolerance thresholds

Engineers in the field of control systems have been recently drawn to the development of creating a hands-free and speech-free controller interface over computers and robotic devices. The primary individuals who would use this type of controller suffer from progressive nervous system diseases or other forms of paralysis that have severely restricted any movement of the limbs. Despite their physical limitations, these same individuals have an uncompromised brain full of cognitive and sensory functions. As a result, one solution to restore mobility and autonomy to the paralyzed is to create a controller that utilizes their brain signals. A brain computer interface (BCI) applies brain signals as input to a controller that will then drive a robot arm or transporter. By linking a specific mental task (i.e. imagine squeezing the right hand) to a command a robot (i.e. make a right turn), users have the ability to navigate an electrically powered wheel chair or robot-aid for themselves. While there is potential to create a wide range of controller commands, brainwaves come with their own set of challenges. These signals are non-stationary and non-linear; meaning, brainwaves constantly vary and are extremely difficult to model. In addition, noise from other involuntary functions (i.e. blinking and facial muscle activation) may bury the unique signals associated to the mental task. To overcome these obstacles, control system engineers have implemented a signal preprocessing step and machine learning approach to these controllers. The combination of selecting the right preprocessor, machine learning algorithm, and training the user to conduct clear mental tasks creates an accurate and responsive BCI controller. The main goal of this project is to design a six-class hybrid BCI controller for a semi-autonomous mobile robotic arm. The controller is designed to operate the robotic base and arm separately. To do this, a set of EEG motor imagery hand and feet signals serves two primary functions: they navigate the robot base in the environment and move a cursor on the robot's camera screen to highlight what object to grab. In addition, a jaw clench, which is an electromyogram (EMG) signal, is used to switch between commanding the base and the arm. Designing a controller with this capability for multiple users requires a compilation of hardware to record/stream brainwaves and software to preprocess and train a machine learning algorithm. A modified 14-channel commercial grade non-invasive electroencephalogram (EEG) headset from Emotiv Epoch was used to output the brain waves of three healthy males (ages 22 - 27) to the computer. Each subject recorded five sessions, each with four tests, of their responses to OpenViBE's stimulus presentation program. The recordings were then uploaded to EEGLAB, an open source MATLAB plug-in, where the signals were preprocessed with filters and the implementation of Independent Component Analysis (ICA). Additionally, EEGLAB was used to plot Event Related Potential (ERP) plots and topographical maps to observe each subject's brain activity. After reviewing all the plots, each subject shared the same behavior in electrodes C1, C3, C5, C2, C4, and C6. For comparison, two machine learning algorithms, linear discriminant analysis (LDA) and relevance vector machine (RVM) were chosen to process and classify the subjects' recordings. The performance for each classifier was recorded for a 2-class, 3-class, 5-class, and 6-class controller. RVM out performed LDA with multi-class controllers. For a 5-class controller, the error rate percentages were: 45% for subject S01, 30.8% for subject S02, and 29.2% for subject S03. With the proper electrodes and machine learning algorithms identified, the official 6-class controller was created with a common spatial pattern (CSP) filter and RVM classifier. It was observed that the accuracy of the controller decreased as the number of classes increased. The 6-class BCI controller was integrated into a virtual model of the semi-autonomous robotic arm where it successfully demonstrated the ability to separately move the base, move the cursor on the robot's camera screen, and activate the action to pick up/drop off an object.

Brain Computer Interface (BCI) technology provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity, allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. Recently, there has been a great progress in the development of novel paradigms for EEG signal recording, advanced methods for processing them, new applications for BCI systems and complete software and hardware packages used for BCI applications. In this book a few recent advances in these areas are discussed.

A real-time brain control interface (BCI) paired with an autonomous robotic system, such as an "AU-PAIR BIMA BOT" is a technology that allows individuals to control the movements of the robot using their brain activity. The BCI system works by recording the electrical activity in the brain, typically using electroencephalography (EEG) sensors, and then translating this activity into commands for the robot to perform. The "AU-PAIR BIMA BOT" is an autonomous robotic system that can be controlled by the BCI. It is designed to assist with daily tasks and activities, such as household chores, and can be programmed to respond to specific commands or patterns of brain activity. The robot is equipped with sensors and cameras that allow it to navigate and interact with its environment. This technology has the potential to greatly enhance the quality of life for individuals with disabilities or mobility impairments, allowing them to perform tasks and interact with their environment in ways that would otherwise be difficult or impossible. Additionally, the technology could also be used for other applications such as gaming, education, and research in the field of human-computer interaction. It's worth noting that currently this type of technology is still in the research and development stage and not yet available for commercial use.