scholarly journals National Institute of Neurological Disorders and Stroke support for brain-machine interface technology

2009 ◽  
Vol 27 (1) ◽  
pp. E14 ◽  
Author(s):  
Joseph J. Pancrazio
Keyword(s):  
Neurological Disorders ◽  
Assistive Devices ◽  
National Institutes Of Health ◽  
Brain Machine Interface ◽  
Multidisciplinary Teams ◽  
Neural Engineering ◽  
Design Development ◽  
Volitional Control ◽  
History Of ◽  
Machine Interface

Brain-machine interfaces (BMIs) offer the promise of restoring communication, enabling control of assistive devices, and allowing volitional control of extremities in paralyzed individuals. Working in multidisciplinary teams, neurosurgeons can play an invaluable role in the design, development, and demonstration of novel BMI technology. At the National Institutes of Health, the National Institute of Neurological Disorders and Stroke has a long history of supporting neural engineering and prosthetics efforts including BMI, and these research opportunities continue today. The author provides a brief overview of the opportunities and programs currently available to support BMI projects.

scholarly journals Coherence analysis of EEG in locomotion using graphs

2017 ◽  
Author(s):  
◽  
G. Quiroz
Keyword(s):  
Assistive Devices ◽  
Brain Machine Interface ◽  
Coherence Analysis ◽  
Connectivity Analysis ◽  
Eeg Signals ◽  
Time Frequency ◽  
Temporal Features ◽  
Machine Interface ◽  
Spatio Temporal ◽  
Motion Intention

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

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

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.

scholarly journals Brain-machine interface (BMI) - application to neurological disorders

2013 ◽  
Vol 53 (11) ◽  
pp. 962-965
Author(s):  
Toshiki Yoshimine ◽  
Takufumi Yanagisawa ◽  
Masayuki Hirata
Keyword(s):  
Neurological Disorders ◽  
Brain Machine Interface ◽  
Machine Interface

scholarly journals Coherence analysis of EEG in locomotion using graphs

2017 ◽  
Author(s):  
◽  
G. Quiroz
Keyword(s):  
Assistive Devices ◽  
Brain Machine Interface ◽  
Coherence Analysis ◽  
Connectivity Analysis ◽  
Eeg Signals ◽  
Time Frequency ◽  
Temporal Features ◽  
Machine Interface ◽  
Spatio Temporal ◽  
Motion Intention

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

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

10.2196/16339 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e16339 ◽  
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

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.

A prototype closed-loop brain–machine interface for the study and treatment of pain

2021 ◽  
Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Machine Interface ◽  
Machine Interface
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