Brain–Computer Interfaces Handbook: Technological and Theoretical Advances provides a tutorial and an overview of the rich and multi-faceted world of Brain–Computer Interfaces (BCIs). The authors supply readers with a contemporary presentation of fundamentals, theories, and diverse applications of BCI, creating a valuable resource for anyone involved with the improvement of people’s lives by replacing, restoring, improving, supplementing or enhancing natural output from the central nervous system. It is a useful guide for readers interested in understanding how neural bases for cognitive and sensory functions, such as seeing, hearing, and remembering, relate to real-world technologies. More precisely, this handbook details clinical, therapeutic and human-computer interfaces applications of BCI and various aspects of human cognition and behavior such as perception, affect, and action. It overviews the different methods and techniques used in acquiring and pre-processing brain signals, extracting features, and classifying users’ mental states and intentions. Various theories, models, and empirical findings regarding the ways in which the human brain interfaces with external systems and environments using BCI are also explored. The handbook concludes by engaging ethical considerations, open questions, and challenges that continue to face brain–computer interface research. Features an in-depth look at the different methods and techniques used in acquiring and pre-processing brain signals, extracting features, and classifying the user's intention Covers various theories, models, and empirical findings regarding ways in which the human brain can interface with the systems or external environments Presents applications of BCI technology to understand various aspects of human cognition and behavior such as perception, affect, action, and more Includes clinical trials and individual case studies of the experimental therapeutic applications of BCI Provides human factors and human-computer interface concerns in the design, development, and evaluation of BCIs Overall, this handbook provides a synopsis of key technological and theoretical advances that are directly applicable to brain–computer interfacing technologies and can be readily understood and applied by individuals with no formal training in BCI research and development.

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

The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems.

Smart wheelchairs with semi or fully autonomous functions, can greatly improve the mobility of persons with physical impairments. However, most are controlled using inputs that require physical manipulation (e.g. joystick controllers) and for persons with severe physical impairments this method of control can be too demanding. A noninvasive brain-computer interface (BCI) technology-based controller could bridge the gap between smart wheelchairs and physically impaired persons with severe conditions. Current BCI-controlled wheelchairs rely on detecting steady-state visually evoked potential (SSVEP) responses as these typically have the greatest data transfer rate. However, this method requires the user to focus on a screen for an extended period of time. This causes strain on the user and takes their attention away from their surroundings, which could be dangerous in a scenario that requires navigation around multiple moving objects. The main goal of this project is to design a hybrid BCI controller using an electroencephalogram (EEG) headset to detect hand motor imagery (MI) and jaw electromyography (EMG) signals to control a smart wheelchair in conjunction with its semi-autonomous capabilities. A controller of this kind is well-known to have low data transfer rates, and therefore has lower accuracy and longer response times as compared to other controllers. However, a properly structured controller hierarchy between the BCI controller and semi-autonomous system is developed to compensate the limitations of the controller's accuracy. OpenViBE, an open source BCI programming application, and a commercial grade EEG headset are used for data acquisition. A multiple common spatial pattern (CSP) filter and linear discriminant analysis (LDA) classifier system are chosen to process and classify the user's brain activity. Users are trained to use the system and classifier profiles are optimized for each user. Two subjects are trained to use the BCI system and the results of their practice are analyzed for this study. The average classifier accuracy for Subject 1 is 0.87 and the average classifier accuracy for Subject 2 is 0.83. Next, a fuzzy logic controller (FLC) controller is created in LabVIEW to convert the classifier data to a signal that is compatible with the semi-autonomous wheelchair system. The performance of the controller scheme is evaluated using the OpenViBE "Replay" script and recorded training data. Positive, negative, and false positive executions are recorded for each subject. Initially, positive rates for both subjects were strong, but false positive rates were too high to be used. The design is iterated by changing the configuration of the LabVIEW script and the rules of the FLC. The configuration with the best positive rates for turn executions is chosen. The average positive rate for turning is 0.68 for Subject 1 and 0.64 for Subject 2. The positive rate for executing the start/stop function, however, was only able to achieve 0.50 for Subject 1 and 0.38 for Subject 2. False positive rates, however, were brought down to levels below 0.20 for both subjects.

This book describes the latest research advances, innovations, and visions in the field of robotics as presented by leading researchers, engineers, and practitioners from around the world at the 14th International Conference on Intelligent Autonomous Systems (IAS-14), held in Shanghai, China in July 2016. The contributions amply demonstrate that robots, machines and systems are rapidly achieving intelligence and autonomy, attaining more and more capabilities such as mobility and manipulation, sensing and perception, reasoning, and decision-making. They cover a wide range of research results and applications, and particular attention is paid to the emerging role of autonomous robots and intelligent systems in industrial production, which reflects their maturity and robustness. The contributions were selected by means of a rigorous peer-review process and highlight many exciting and visionary ideas that will further galvanize the research community and spur novel research directions. The series of biennial IAS conferences, which began in 1986, represents a premiere event in the field of robotics.

This book provides a cutting-edge overview of the latest developments in Brain-Computer-Interfaces (BCIs), reported by leading research groups. As the reader will discover, BCI research is moving ahead rapidly, with many new ideas, research initiatives, and improved technologies, e.g. BCIs that enable people to communicate just by thinking – without any movement at all. Several different groups are helping severely disabled users communicate using BCIs, and BCI technology is also being extended to facilitate recovery from stroke, epilepsy, and other conditions. Each year, hundreds of the top BCI scientists, engineers, doctors, and other visionaries compete for the most prestigious honor in the BCI research community: the annual BCI Award. The 2013 BCI Award competition was by far the most competitive, with over 160 research groups vying for a nomination. The chapters of this book summarize the ten projects that were nominated, in particular the winning project, and analyses how these reflect general trends in BCI development. Each project summary includes an introduction, description of methods, results, and also includes newer work completed after the project was entered for the competition. The texts are presented in accessible style with numerous supporting pictures, graphs, and figures.

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented.

Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situations. What will BCIs in the future be like? Who will use them, and why? This book, written by many of the top BCI researchers and developers, reviews the latest progress in the different components of BCIs. Chapters also discuss practical issues in an emerging BCI enabled community. The book is intended both for professionals and for interested laypeople who are not experts in BCI research.