Invasive or Noninvasive: Understanding Brain-Machine Interface Technology [Conversations in BME (original) (raw)
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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
Physiological reviews, 2017
Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movement...
Brain–Machine Interface Engineering
Synthesis Lectures on Biomedical Engineering, 2007
What has the past decade taught us about mankind's ability to interface with and read information from the brain? Looking back on our experiences, the salient recollection is how ill-prepared the present theories of microelectronic circuit design and signal processing are for building interfaces and interpreting brain's activity. Although there is plenty of room for future improvement, the combination of critical evaluation of current approaches and a vision of nueroengineering are helping us develop an understanding on how to read the intent of motion in brains. The flow of ideas and discovery conveyed in this book is quite chronological, starting back in 2001 with a multi-university research project lead by Dr. Miguel Nicolelis of Duke University to develop the next-generation BMIs. The series of engineering developments explained in this book were made possible by the collaboration with Miguel, his contagious enthusiasm, vision, and brilliant experimentalism, that have led us in a journey of discovery in new theories for interfacing with the brain. Part of the results presented here also utilize data collected in his laboratory at Duke University. It was also a journey of innovation shared with colleagues in ECE. Dr. John Harris was instrumental in designing the chips and proposing new devices and principles to improve the performance of current devices. Dr. Karl Gugel helped develop the DSP hardware and firmware to create the new generation of portable systems. We were fortunate to count with the intelligence, dedication, and hard work of many students. Dr. Justin Sanchez came on board to link his biomedical knowledge with signal processing, and his stay at University of Florida has expanded our ability to conduct research here. Dr. Sung-Phil Kim painstakingly developed and evaluated the BMI algorithms. Drs. Deniz Erdogmus and Yadu Rao helped with the theory and their insights. Scott Morrison, Shalom Darmanjian, and Greg Cieslewski developed and programmed the first portable systems for online learning of neural data. Later on, our colleagues Dr. Toshi Nishida and Dr. Rizwan Bashirullah open up the scope of the work with electrodes and wireless systems. Now, a second generation of students is leading the push forward; Yiwen Wang, Aysegul Gunduz, Jack DiGiovanna, Antonio Paiva, and Il Park are advancing the scope of the work with spike train Foreword v modeling. This current research taking us to yet another unexplored direction, which is perhaps the best indication of the strong foundations of the early collaboration with Duke. This book is only possible because of the collective effort of all these individuals. To acknowledge appropriately their contributions, each chapter will name the most important players. Jose C. Principe and Justin C. Sanchez vi BRaIN-MaChINE INTERFaCE ENgINEERINg 1 The study of repair and regeneration of the central nervous system is quite broad and includes contributions from molecular/cellular neuroscience, tissue engineering, and materials science. For a comprehensive review of the application of each of these to the repair of the nervous system, see References [1-4].
Brain-machine interfaces: an overview
Translational Neuroscience, 2014
Brain-machine interfaces (BMIs) hold promise to treat neurological disabilities by linking intact brain circuitry to assistive devices, such as limb prostheses, wheelchairs, artificial sensors, and computers. BMIs have experienced very rapid development in recent years, facilitated by advances in neural recordings, computer technologies and robots. BMIs are commonly classified into three types: sensory, motor and bidirectional, which subserve motor, sensory and sensorimotor functions, respectively. Additionally, cognitive BMIs have emerged in the domain of higher brain functions. BMIs are also classified as noninvasive or invasive according to the degree of their interference with the biological tissue. Although noninvasive BMIs are safe and easy to implement, their information bandwidth is limited. Invasive BMIs hold promise to improve the bandwidth by utilizing multichannel recordings from ensembles of brain neurons. BMIs have a broad range of clinical goals, as well as the goal t...
JOURNAL OF MEDICAL INTERNET RESEARCH, 2019
The first attempts to translate neuronal activity into commands to control external devices were made in monkeys yet in 1960s. After that, during 1960-1970, the biological feedback was realized in monkeys, to provide voluntary control of the firing rate of cortical neurons. The term "brain-computer interface" appeared only in earlier 1970s. The brain-computer interface is usually referred to as a "brain-machine interface" in invasive studies. Nowadays, the brain-computer interface and brain-machine interface research and applications are considered one of the most exciting interdisciplinary areas of science and technology. In particular, brain-computer interfaces are very promising for neurorehabilitation of sensory and motor disabilities, neurocommunication, exoskeletons, cognitive state evaluation, etc. Advanced mathematical methods for extraction and classification of neuronal activity features hold out hope for the future use of brain-computer interfaces in everyday life. At the same time, the lack of effective invasive neuroimaging techniques providing a high-resolution neural activity recording for medical purposes limits the brain-machine interface implementation in clinics. In their paper, Elon Musk and Neuralink have successfully addressed the major issues hampering the next generation of invasive brain-computer interface (or brain-machine interface) development by introducing a novel integrated platform enabling a high-quality registration of thousands of channels. Their device contains arrays of flexible electrode threads with up to 3072 electrodes per array, distributed across 96 threads. To overcome a surgical limitation, the authors have built a neurosurgical robot that inserts 6 threads per minute with a micrometer spatial precision. To increase the biocompatibility, they created a neurosurgical robot, which implants polymer probes much faster and more safely than existing surgical approaches. Using this platform in freely moving rats, the authors report a spiking yield of up to 85.5%. Although the developed system is considered an effective platform for research in rodents, it can serve as an invasive neurointerface prototype for clinical applications. Specifically, multielectrode neurointerfaces may become the basis for new communication systems and advanced assistive technologies for paralyzed people as well as control external devices and interact with the entire environment, eg, by integrating into new fast developed technologies, such as Smart Home and Internet of Things. Moreover, the brain-computer interface applications are very promising for detecting hidden information in the user's brain, which cannot be revealed by conventional communication channels.
Non invasive brain-machine interfaces-Final Report
2005
This document represents the final report of the study "Non invasive brain-machine interfaces" performed by the Interdepartmental Research Center "E. Piaggio" of the University of Pisa within the ARIADNA framework of activities promoted by the European Space Agency (ESA). Contents of the report are organized as follows. The first part presents a literature survey on the state of the art of brain-machine interfaces (BMI), with a particular emphasis on the non-invasive types. In order to discuss potential benefits deriving from the use even of additional interfaces, conceived as complementary and auxiliary for BMI, the second part reviews different types of non-invasive man-machine interfaces. Their working principles, implementations, possible applications and typical features are discussed. Such additional interfaces are considered as a useful help, especially for multi-task activities. The report then presents a selection of the most promising and feasible non-invasive BMI concept for space applications, as well as the most interesting man-machine interface concepts capable of working as auxiliary and complementary tools. In particular, selected concepts consist of EEG-based BMI, to be eventually used in combination with interfaces based on speech recognition, EMG activation and motion capture and gesture recognition. The final part reports potential fields of space applications for such types of interfaces.
Cortically controlled brain-machine interface
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2005
Over the past ten years, we have tested and helped develop a multi-electrode array for chronic cortical recordings in behaving non-human primates. We have found that it is feasible to record from dozens of single units in the motor cortex for extended periods of time and that these signals can be decoded in a closedloop, real-time system to generate goal-directed behavior of external devices. This work has culminated in a FDA clinical trial that has demonstrated that a tetraplegic patient can voluntarily modulate motor cortical activity in order to move a computer cursor to visual targets. Further advances in BMI technology using non-human primates have focused on using multiple modes of control from signals in different cortical areas. We demonstrate that primary motor cortical activity may be optimized for continuous movement control whereas signals from the premotor cortex may be better suited for discrete target selection. We propose a hybrid BMI whereby decoding can be voluntar...
Non-invasive brain-machine interaction
International Journal of Pattern Recognition and Artificial Intelligence, 2008
The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain electrical activity online, several groups have designed brain-actuated devices that provide alternative channels for communication, entertainment and control. Thus, a person can write messages using a virtual keyboard on a computer screen and also browse the internet. Alternatively, subjects can operate simple computer games, or brain games, and interact with educational software. Work with humans has shown that it is possible for them to move a cursor and even to drive a wheelchair. This paper briefly reviews the field of BCI, with a focus on non-invasive systems based on electroencephalogram (EEG) signals. It also describes three brain-actuated devices we have developed: a virtual keyboard, a brain game, and a wheelchair. Finally, it shortly discusses current research directions we are pursuing in order to improve the performance and robustness of our BCI system, especially for real-time control of brainactuated robots.
Brain-Machine and Brain-Computer Interfaces
Stroke, 2004
The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain–computer and brain–machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors’ preliminary experience with a prototype brain–computer interface is reported.
Future developments in brain-machine interface research
Clinics, 2011
Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the É cole Polytechnique Fé dé rale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.