Defining Brain-Machine Interface Applications by Matching Interface Performance With Device Requirements (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...
FEB-24-30-Brain Machine Interface through Visually Guided Electrical Brain Responses
A brain-machine interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles. It is a new communication link between a functioning human brain and the outside world. These are electronic interfaces with the brain, which has the ability to send and receive signals from the brain. BMI uses brain activity to command, control, actuate and communicates with the world directly through brain integration with peripheral devices and systems. The signals from the brain are taken to the computer via the implants for data entry without any direct brain intervention. BMI transforms mental decisions and reactions into control signals by analyzing the bioelectrical brain activity. While linking the brain directly with machines was once considered science fiction, advances over the past few years have made it increasingly viable. An immediate goal of brain-machine interface study is to provide a way for people with damaged sensory/motor functions to use their brain to control artificial devices and restore lost capabilities. By combining the latest developments in computer technology and hi-tech engineering, paralyzed persons will be able to control a motorized wheel chair, computer painter, or robotic arm by thought alone.
Oxford Scholarship Online, 2018
A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initial...
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Human brain mapping, 2017
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controllin...
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...
2015
A brain-machine interface (BMI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortic al plasticity y of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.
Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System
IEEE Systems, Man, and Cybernetics Magazine, 2015
brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]-[5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop. An important aspect of a BMI is the capability to distinguish between different patterns of brain activity, with each being associated with a particular intention or mental task. Hence, adaptation is a key component of a BMI because, on the one hand, users must learn to modulate their neural activity to generate distinct brain patterns, while, on the other hand, machine-learning techniques need to discover the individual brain patterns characterizing the mental tasks executed by the user. In essence, a BMI is a two-learner system that must engage in a mutual adaptation process [6], [7]. Future neuroprosthetics-robots and exoskeletons controlled via a BMI-will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired limb functions because they are controlled by the same neural signals as their natural counterparts. However, the robust and natural interaction of subjects with prostheses over long periods of time remains a major challenge. To tackle this challenge, we can take inspiration from natural motor control, where
Developments in brain–machine interfaces from the perspective of robotics
Human Movement Science, 2009
Many patients suffer from the loss of motor skills, resulting from traumatic brain and spinal cord injuries, stroke, and many other disabling conditions. Thanks to technological advances in measuring and decoding the electrical activity of cortical neurons, brainmachine interfaces (BMI) have become a promising technology that can aid paralyzed individuals. In recent studies on BMI, robotic manipulators have demonstrated their potential as neuroprostheses. Restoring motor skills through robot manipulators controlled by brain signals may improve the quality of life of people with disability. This article reviews current robotic technologies that are relevant to BMI and suggests strategies that could improve the effectiveness of a brain-operated neuroprosthesis through robotics.
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...