The Brain-Machine Interface (BMI) vs Impairment

AbstractMinimally invasive electrodes of cellular scale that approach a bio-integrative level of neural recording could enable the development of scalable brain machine interfaces that stably interface with the same neural populations over long period of time.In this paper, we designed and created NeuroRoots, a bio-mimetic multi-channel implant sharing similar dimension (10µm wide, 1.5µm thick), mechanical flexibility and spatial distribution as axon bundles in the brain. A simple approach of delivery is reported based on the assembly and controllable immobilization of the electrode onto a 35µm microwire shuttle by using capillarity and surface-tension in aqueous solution. Once implanted into targeted regions of the brain, the microwire was retracted leaving NeuroRoots in the biological tissue with minimal surgical footprint and perturbation of existing neural architectures within the tissue. NeuroRoots was implanted using a platform compatible with commercially available electrophysiology rigs and with measurements of interests in behavioral experiments in adult rats freely moving into maze. We demonstrated that NeuroRoots electrodes reliably detected action potentials for at least 7 weeks and the signal amplitude and shape remained relatively constant during long-term implantation.This research represents a step forward in the direction of developing the next generation of seamless brain-machine interface to study and modulate the activities of specific sub-populations of neurons, and to develop therapies for a plethora of neurological diseases.

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ECG technology for the brain–machine interface

Handbook of Bioelectronics ◽

10.1017/cbo9781139629539.034 ◽

2015 ◽

pp. 344-351

Author(s):

Ajay Bharadwaj

Keyword(s):

Brain Machine Interface ◽

Machine Interface ◽

The Brain

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Improved multi-unit decoding at the brain–machine interface using population temporal linear filtering

Journal of Neural Engineering ◽

10.1088/1741-2560/7/4/046012 ◽

2010 ◽

Vol 7 (4) ◽

pp. 046012 ◽

Cited By ~ 5

Author(s):

D J Herzfeld ◽

S A Beardsley

Keyword(s):

Brain Machine Interface ◽

Linear Filtering ◽

Machine Interface ◽

The Brain

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Neuronal activity and learning in local cortical networks are modulated by the action-perception state

10.1101/537613 ◽

2019 ◽

Author(s):

Ben Engelhard ◽

Ran Darshan ◽

Nofar Ozeri-Engelhard ◽

Zvi Israel ◽

Uri Werner-Reiss ◽

...

Keyword(s):

Firing Rate ◽

Chaotic Regime ◽

Learning Activity ◽

Brain Machine Interface ◽

Action Perception ◽

Current Action ◽

Target Neuron ◽

Machine Interface ◽

Neuronal Patterns ◽

The Brain

SummaryDuring sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

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Use of Brain-Machine Interface technology in the rehabilitation of patients

Research Society and Development ◽

10.33448/rsd-v9i11.10016 ◽

2020 ◽

Vol 9 (11) ◽

pp. e84691110016

Author(s):

Bruna Corrêa Nolêto ◽

Fernanda Rodrigues de Araújo Paiva Campelo ◽

Karleth Costa Spíndola Rodrigues ◽

Letice Mendes Ribeiro ◽

Mauricio Salviano

Keyword(s):

Brain Activity ◽

Brain Machine Interface ◽

Human Beings ◽

Motor Rehabilitation ◽

Technological Revolution ◽

Innovative Technologies ◽

Motor Disability ◽

Machine Interface ◽

The Subject ◽

The Brain

In the last few decades, there have been advances in the field of innovative technologies used for the rehabilitation of people with a motor disability. A great example is the Brain-Machine Interface (BMI) technologies, which allow the control of machines through the brain activity of individuals and contributes to a reorganization of their motor and sensory systems. Thus, several evidences have suggested the use of technologies in the rehabilitation of these patients. The aim of this study was to perform a literature review on the use of technologies applied to motor rehabilitation. To carry out this study, a search for scientific articles was performed in the Pubmed, Scielo and Lilacs databases, in addition to the dissertations and theses found on the CAPES database. There were a total of 24 references, published between 2002 and 2020. According to the literature studied, there is an increase in resources that use technologies as therapeutic options. Many of the conventional interventions are being replaced or associated with these innovative technologies. With the advent of BMI technology and its use in human beings, a technological revolution can be observed in several biomedical areas, thus allowing a multidisciplinary application in the rehabilitation of motor, sensory or cognitive functions in patients. Despite the advances, this subject still shows controversies and before a broad recommendation, more randomized studies and a greater ethical recommendation on the subject will be needed.

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The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels” (Preprint)

10.2196/preprints.16339 ◽

2019 ◽

Author(s):

Robert F Kirsch ◽

A Bolu Ajiboye ◽

Jonathan P Miller

Keyword(s):

Neurological Disorders ◽

Brain Machine Interface ◽

Loss Of Function ◽

Brain Machine Interfaces ◽

Electrode Interface ◽

Machine Interface ◽

Almost All ◽

Neuroprosthetic Devices ◽

The Brain

UNSTRUCTURED Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.

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From thought to action: The brain–machine interface in posterior parietal cortex

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1902276116 ◽

2019 ◽

Vol 116 (52) ◽

pp. 26274-26279 ◽

Cited By ~ 3

Author(s):

Richard A. Andersen ◽

Tyson Aflalo ◽

Spencer Kellis

Keyword(s):

Parietal Cortex ◽

Posterior Parietal Cortex ◽

The Body ◽

Assistive Device ◽

Brain Machine Interface ◽

Wide Range ◽

Cord Injury ◽

Posterior Parietal ◽

Machine Interface ◽

The Brain

A dramatic example of translational monkey research is the development of neural prosthetics for assisting paralyzed patients. A neuroprosthesis consists of implanted electrodes that can record the intended movement of a paralyzed part of the body, a computer algorithm that decodes the intended movement, and an assistive device such as a robot limb or computer that is controlled by these intended movement signals. This type of neuroprosthetic system is also referred to as a brain–machine interface (BMI) since it interfaces the brain with an external machine. In this review, we will concentrate on BMIs in which microelectrode recording arrays are implanted in the posterior parietal cortex (PPC), a high-level cortical area in both humans and monkeys that represents intentions to move. This review will first discuss the basic science research performed in healthy monkeys that established PPC as a good source of intention signals. Next, it will describe the first PPC implants in human patients with tetraplegia from spinal cord injury. From these patients the goals of movements could be quickly decoded, and the rich number of action variables found in PPC indicates that it is an appropriate BMI site for a very wide range of neuroprosthetic applications. We will discuss research on learning to use BMIs in monkeys and humans and the advances that are still needed, requiring both monkey and human research to enable BMIs to be readily available in the clinic.

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