Practical Artifact Removal Brain-Computer Interface System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Neuroprostheses as an Element of an Eclectic Approach to Intervention in Neurorehabilitation

Improvements in the effectiveness of contemporary neurorehabilitation emphasize the need for a shift from a specific approach to intervention to an eclectic approach to intervention. The novel strategies of brain-computer interfaces' and neuroprostheses' application in an eclectic approach to intervention may be regarded as leading the way in clinical practice development. There is a limited amount of evidence both in the areas of theoretical principles and clinical applications, but it seems the application of various rehabilitation methods and techniques may effectively support the outcomes of the BCI's and NP's use. The author aims investigates the extent to which the available opportunities are being exploited, including current and potential future applications of neuroprostheses within an eclectic approach to intervention in neurorehabilitation.

Are We the Robots?

We experience and interact with the world through our body. The founding father of computer science, Alan Turing, correctly realized that one of the most important features of the human being is the interaction between mind and body. Since the original demonstration that electrical activity of the cortical neurons can be employed to directly control a robotic device, the research on the so-called Brain-Machine Interfaces (BMIs) has impressively grown. For example, current BMIs dedicated to both experimental and clinical studies can translate raw neuronal signals into computational commands to reproduce reaching or grasping in artificial actuators. These developments hold promise for the restoration of limb mobility in paralyzed individuals. However, as the authors review in this chapter, before this goal can be achieved, several hurdles have to be overcome, including developments in real-time computational algorithms and in designing fully implantable and biocompatible devices. Future investigations will have to address the best solutions for restoring sensation to the prosthetic limb, which still remains a major challenge to full integration of the limb into the user's self-image.

Brain-Computer Interfaces for Control of Upper Extremity Neuroprostheses in Individuals with High Spinal Cord Injury

For individuals with tetraplegia, restoring limited or missing grasping function is the highest priority. In patients with high Spinal Cord Injury (SCI) and a lack of surgical options, restricted upper extremity function can be improved with the use of neuroprostheses based on Functional Electrical Stimulation (FES). Grasp neuroprostheses with different degrees of complexity and invasiveness exist, although few models are available for routine clinical application. Hybrid systems combining FES with orthoses hold promise for restoring completely lost upper extremity function. Novel user interfaces integrating biosignals from several sources are needed to make full use of the many degrees of freedom of hybrid neuroprostheses. Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) are an emerging technology that may serve as a valuable adjunct to traditional control interfaces. This chapter provides an overview of the current state of the art of BCI-controlled upper-extremity neuroprostheses and describes the challenges and promises for the future.

Lower-Limb Neuroprostheses

Regaining lower-limb functionality such as walking is one of the highest priorities among all the disabilities of paraplegics following Spinal Cord Injury (SCI). Though the ultimate recovery would be repairing or regenerating new axons across the spinal lesion (potentially by stem cells or other transplants and neurotropic factors), challenges to achieve this as well as recent technological advancements demand the development of new neuroprosthetic devices to restore such motor functions following the injuries. In this chapter, the authors discuss available therapies for the rehabilitation of SCI paraplegics and some new potential interventions that still require clinical tests. They also propose brain-machine-spinal cord interface as a future neuroprosthesis following motor complete SCI.

Models of Cooperation between Medical Specialists and Biomedical Engineers in Neuroprosthetics

The development of novel technologies associated with neuroprosthetics and their clinical applications needs interdisciplinary knowledge, including not only medical sciences, but IT, biomedical engineering, biocybernetics, and robotics. The variability of possible neurological deficits, interventions, and even scales—from nanotechnology up to rehabilitation robots and brain-computer-interface controlled exoskeletons as whole-body neuroprostheses—makes this task very difficult. Current models of education and cooperation within interdisciplinary therapeutic teams only concern medical specialists. This chapter tries to answer the question, how can biomedical engineers be incorporated into research and clinical practice in neuroprosthetics considering the various aforementioned factors, necessary changes in educational processes, ethical issues, and associated organizational problems?

Chances for and Limitations of Brain-Computer Interface use in Elderly People

Recent demographic prognoses show tendencies toward a significant increase in the number of elderly people, especially in developed countries. This makes geriatric therapy, rehabilitation, and care difficult, especially with maintaining as long as possible the highest quality of life and independence in activities of daily living. Lack of specialized personnel and financial shortages may cause increased application of Assistive Technology (AT) and associated control devices. The most advanced current devices for diagnosis, communication, and control purposes are perceived Brain-Computer Interfaces (BCIs). BCIs use brain-derived bioelectrical signals as an input to enable diagnosis, communication, and/or control (e.g. neuroprostheses, medical robots, wheelchairs, whole integrated environments) without any movement. BCIs are regarded as novel solutions offering another breakthrough in everyday life, care, therapy, and rehabilitation in patients with severe sensory and neuropsychological deficits. However, particular issues in the area of BCIs use in elderly people should be emphasized, including influence of neurodegenerative disorders accompanied with secondary changes resulting from other medical problems (e.g. heart diseases, hypertension, diabetes mellitus, and osteoporosis), co-occurence of various drug therapies, etc. This chapter investigates the extent to which the available opportunities are being exploited, including both chances and limitations, medical, technical, psychological, societal, ethical, and legal issues.

Sensors for Motor Neuroprosthetics

Clinical applications of Functional Electrical Stimulation (FES) provide both functional and therapeutic benefits. To enhance the functionality of FES systems and to improve the control of the activated muscles through open-loop or feedback controllers, solutions to gather information about the status of the system in real time and to easily detect the intention of the subject have to be optimized. This chapter summarizes the state of art of sensors used in motor neuroprostheses. These sensors can be classified in two categories: sensors of biological signals, such as electromyogram, electroencephalogram, electroneurogram, eye tracking, and voice control, and sensors of non-biological signals, such as sensors of force/pressure (e.g. force sensitive resistors and strain gauges) and sensors of movement (e.g. accelerometers, electrogoniometers, inertial measurement units, and motion capture systems). Definitions, advantages and disadvantages, and some example of applications are reported for each sensor. Finally, guidelines to compare sensors for the design of motor neuroprostheses are drawn.

Retinal Prosthetics

In this chapter, the authors briefly introduce the neuroanatomical basis for vision and explain how the retina processes visual information. Pathology of the retina and the conditions that cause photoreceptor degeneration and lead to blindness are then given, followed by the main part of the chapter in which they present an overview of the concept of restoring vision with visual prosthetics. The focus is specifically on retinal prostheses and electrical stimulation parameters used with these devices. Both in vitro and in vivo animal studies from the last decade are surveyed, together with the latest results from human trials conducted in multiple research centers worldwide. Eventually, the authors discuss current open issues of the technology, such as implant placement, biocompatibility, electrode design, and safety. In the final section, they give their opinion on future developments and perspectives.

Neuroprosthetics

Neuroprostheses use electric stimuli to stimulate neural structures, muscles, or receptors in order to support, augment, or partly restore the respective disordered or lost function. The objective is to help the patient to participate in everyday life. The use of a neural prosthesis can improve the quality of life of the person concerned. The future of neuroprosthetics is challenging as well as interesting as it deals with several latest technological advancements that connect both biology and technology together. This chapter briefly explains the current advancements and future challenges related to Neuroprosthetics research.