This research presents a non-invasive hybrid EEG-based brain computer interface (BCI) system aimed at providing a technological solution for individuals suffering from paralysis or severe motor disability. It also aims to enable individuals to communicate with surrounding environment vita thoughts and stimuli.

This book aims to bring to the reader an overview of different applications of brain-computer interfaces (BCIs) based on more than 20 years of experience working on these interfaces. The author provides a review of the human brain and EEG signals, describing the human brain, anatomically and physiologically, with the objective of showing some of the patterns of EEG (electroencephalogram) signals used to control BCIs. It then introduces BCIs and different applications, such as a BCI based on ERD/ERS Patterns in α rhythms (used to command a robotic wheelchair with an augmentative and alternative communication (AAC) system onboard it); a BCI based on dependent-SSVEP to command the same robotic wheelchair; a BCI based on SSVEP to command a telepresence robot and its onboard AAC system; a BCI based on SSVEP to command an autonomous car; a BCI based on independent-SSVEP (using Depth-of-Field) to command the same robotic wheelchair; the use of compressive technique in SSVEP-based BCI; a BCI based on motor imagery (using different techniques) to command a robotic monocycle and a robotic exoskeleton; and the first steps to build a neurorehabilitation system based on motor imagery of pedalling together an in immersive virtual environment. This book is intended for researchers, professionals and students working on assistive technology.

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.

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.

The idea of interfacing minds with computers has captured human imagination for a long time. Recent developments in neuroscience and engineering have made this concept a possibility, opening the door to restoring and potentially growing human physical and mental capabilities. Medical applications such as cochlear implants for deaf patients and deep brain stimulation for Parkinson's disease are becoming increasingly common. Brain-computer interfaces (BCIs) (also known as brain-machine interfaces or BMIs) are currently being explored in applications as diverse as defense, lie detection, alertness monitoring, telepresence, gaming, education, art, and human enhancement. By the end of reading this book, you will master the discussion about the following topics of Brain Computer Interface: Definitions UCLA and DARPA Neuro-Prosthetics Applications Neuromodulation History Electroencephalography (EEG) Brain Computer Interface challenge Brain/Neural Computer Interaction (BNCI) project Contingent Negative Variation (CNV) The Brain Computer Interface Society BCI Versus Neuro Prosthetics Animal Brain Computer Interface Research Phillip Kennedy's Research Yang Dan's Research Miguel Nicolelis' Research Donoghue, Schwartz, Andersen Research Carmena and colleagues Research Lebedev and colleagues Research General-Purpose Brain Computer Interface Research Framework Brain Machine Interface (BMI) Passive Brain Computer Interface Invasive Brain Computer Interfaces Treat Non-Congenital Blindness Restore Mobility in Disabled Individuals Partially invasive Brain Computer Interfaces Electrocorticography (ECoG) Light Reactive Imaging Brain Computer Interface Non-invasive Brain Computer Interface Non-Electroencephalography (EEG)-based brain–computer interface Pupil-Size Oscillation Functional Near Infrared Spectroscopy Electroencephalography (EEG)-based brain-computer interface Advanced Functional Neuroimaging Dry Active Electrode Array SSVEP Mobile Electroencephalography (EEG) Brain Computer Interface Cellular-based Brain Computer Interface Mobile Brain Computer Interface Devices Limitations Prosthesis and Regulation of the World Brain Computer Interface in Military Do It Yourself and Open-Source Brain Machine Interface Open Brain Programming Interface Reconstruction of Human Vision Brain Computer Interface Control Strategies in Neurogaming Motor Imagery Bio/Neurofeedback for Passive Brain Computer Interface Visual Evoked Potential (VEP) Synthetic telepathy/silent communication DARPA Silent Talk Objective Brain-Based Communication Using Imagined Speech First Direct Electronic Contact Experiment Conducted Between Two Humans' Nervous Systems Produce Morse Code Using Electroencephalography (EEG) Transmission of Electroencephalography (EEG) Signals Over the Internet Cell-Culture Brain Computer InterfaceS Caltech First Neurochip Artificial or Prosthetic Hippocampus Neurochip Rat Brain Neurons Fly an F-22 Fighter Jet Aircraft Simulator Ethical Considerations Current Brain Machine Interfacess Are Away from The Ethical Problems Brain Computer Interface In Medical and Pharmaceutical Research Low-cost Brain Computer Interface Sony 2006 NeuroSky 2007 OCZ 2008 Final Fantasy 2008 Uncle Milton Industries 2009 Emotiv 2009 Neurowear's "Necomimi" 2012 They Shall Walk 2014 Open-Source Brain Computer Interface 2016 Neuralink 2020 Future directions Disorders of consciousness (DOC) Motor Recovery Functional Brain Mapping Flexible Devices Neural Dust

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.

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.

Brain-Computer Interfacing, Volume 168, not only gives readers a clear understanding of what BCI science is currently offering, but also describes future expectations for restoring lost brain function in patients. In-depth technological chapters are aimed at those interested in BCI technologies and the nature of brain signals, while more comprehensive summaries are provided in the more applied chapters. Readers will be able to grasp BCI concepts, understand what needs the technologies can meet, and provide an informed opinion on BCI science. - Explores how many different causes of disability have similar functional consequences (loss of mobility, communication etc.) - Addresses how BCI can be of use - Presents a multidisciplinary review of BCI technologies and the opportunities they provide for people in need of a new kind of prosthetic - Offers a comprehensive, multidisciplinary review of BCI for researchers in neuroscience and traumatic brain injury that is also ideal for clinicians in neurology and neurosurgery

This book presents a complete overview of the main EEG-based Brain-Computer Interface (BCI) paradigms and the related practical solutions for their design, prototyping, and testing. Readers will explore active, reactive, and passive BCI paradigms, with an emphasis on the operation for developing solutions, addressing the need for customization. Readers will familiarize themselves with the main steps for the realization of low-cost wearable BCIs which include: identification of the most suitable neuro signals for a specific application; definition of the hardware, firmware, and software, with a focus on wearable, non-invasive, and low-cost solutions; development of algorithms for data processing and classification; and, lastly, experimental campaigns for the validation of the prototyped solutions. BCI systems based on electroencephalography (EEG) are investigated and a complete overview of all BCI paradigms is offered. The aim of this book is to drive the reader, from the beginning to the end, along a research-and-development process of a working BCI prototype. This book is a guide for designers, biomedical engineers, students, biotechnologists, and those in the biomedical instrumentation field that would like to conceive, design, prototype, and test an innovative low-cost wearable EEG-based BCI.

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems.