scholarly journals Preserved motor representations after paralysis

2021 ◽  
Author(s):  
Charles Guan ◽  
Tyson Aflalo ◽  
Carey Zhang ◽  
Emily R. Rosario ◽  
Nader Pouratian ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Plasticity ◽  
Lived Experiences ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Population Activity ◽  
Structure And Dynamics ◽  
Complete Paralysis ◽  
Motor Representations ◽  
Virtual Fingers

Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure and dynamics that matched the representational structure, previously identified in able-bodied individuals. The finger representational structure was consistent during extended use, even though the structure contributed to BCI decoding errors. Our results suggest that motor representations are remarkably stable, even after complete paralysis. BCIs re-engage these preserved representations to restore lost motor functions.

A Closed-Loop Brain–Computer Interface Triggering an Active Ankle–Foot Orthosis for Inducing Cortical Neural Plasticity

2014 ◽  
Vol 61 (7) ◽  
pp. 2092-2101 ◽  
Author(s):  
Ren Xu ◽  
Ning Jiang ◽  
Natalie Mrachacz-Kersting ◽  
Chuang Lin ◽  
Guillermo Asin Prieto ◽  
...  
Keyword(s):  
Neural Plasticity ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Ankle Foot Orthosis ◽  
Foot Orthosis

scholarly journals User Specific Efficiency Parameters for Brain Computer Interface (BCI) based Upper Limb Stroke Rehabilitation System

2021 ◽  
Vol 9 (4) ◽  
pp. 524-528
Keyword(s):  
Neural Plasticity ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Large Population ◽  
Computer Interface ◽  
Computer Gaming ◽  
Middle Income ◽  
Middle Income Countries ◽  
Overall Efficiency ◽  
Low And Middle Income

Rehabilitation after stroke through conventional manner is not quite successful due to a number of patient related issues including lack of interest in lengthy exercises, cost of therapy and dependency on healthcare professionals. In addition, around 50% of stroke survivors worldwide belong to the low and middle income countries that are unable to afford expensive rehabilitation systems. Advancements in Brain Computer Interface (BCI) technology enabling the researchers to design and develop BCI based strokerehabilitation systems by exploiting neural plasticity. This is achieved via Electroencephalogram (EEG) based computer gaming rehabilitation exercises through Motor Imagery (MI) to achieve successful neural plasticity. However, current research is largelybased on expensive bio-signal amplifiers and processing hardware that are beyond the affordability of a large population of stroke patients living in low and middle-income countries. Moreover, the efficiency of BCI based stroke rehabilitation systems thatare generally considered as the accuracy of EEG signal classifications is not the only parameter to rate the efficiency.Since the requirements of BCI based rehabilitation therapy are highly subject specific, efficiency of such systems also depends on many user specific features related to cost and performance.This paper describes a research that proposes a number of parameters for cost and efficiency along with their weightage set by the domestic users to determine the overall efficiency of the system.Inputs from different groups of users were obtained that are classified as deserving class, middle class and rich class. Results indicated that the users of different groups are giving different weights to different performance and cost parameters. The overall efficiency requirements are therefore having different meanings for different classes of users

scholarly journals Потоковая обработка многоканальных сигналов электроэнцефалограммы с использованием технологии параллельных вычислений на графических процессорах NVIDIA CUDA

2018 ◽  
Vol 44 (10) ◽  
pp. 103
Author(s):  
В.В. Грубов ◽  
В.О. Недайвозов
Keyword(s):  
Neural Activity ◽  
Brain Computer Interface ◽  
Spectral Characteristics ◽  
Cognitive Activity ◽  
Computer Interface ◽  
Computing Technology ◽  
Eeg Signals ◽  
Nvidia Cuda ◽  
Eeg Data

AbstractProspects of using parallel computing technology (PaCT) methods for the stream processing and online analysis of multichannel EEG data are considered. It is shown that the application of PaCT to calculation and evaluation of spectral characteristics of EEG signals makes online determination of changes in the energy of the main rhythms of neural activity in various parts of the cerebral cortex possible. The possibility of implementing the PaCT algorithm with CUDA C library and its use in a modern brain–computer interface (BCI) for cognitive-activity monitoring in the course of visual perception.

scholarly journals Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity

2020 ◽  
Vol 4 (7) ◽  
pp. 672-685 ◽  
Author(s):  
Alan D. Degenhart ◽  
William E. Bishop ◽  
Emily R. Oby ◽  
Elizabeth C. Tyler-Kabara ◽  
Steven M. Chase ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Low Dimensional

scholarly journals De novo brain-computer interfacing deforms manifold of populational neural activity patterns in human cerebral cortex

2021 ◽  
Author(s):  
Seitaro Iwama ◽  
Zhang Yichi ◽  
Junichi Ushiba
Keyword(s):  
De Novo ◽  
Brain Computer Interface ◽  
Activity Patterns ◽  
Computer Interface ◽  
Neural Population ◽  
Human Cerebral Cortex ◽  
Population Activity ◽  
Motor Behaviors ◽  
Brain Computer Interfacing ◽  
Human Brains

Human brains are capable of modulating innate activities to adapt to novel environmental stimuli; for sensorimotor cortices (SM1) this means acquisition of a rich repertoire of motor behaviors. We investigated the adaptability of human SM1 motor control by analyzing net neural population activity during the learning of brain-computer interface (BCI) operations. We found systematic interactions between the neural manifold of cortical population activities and BCI classifiers; the neural manifold was stretched by rescaling motor-related features of electroencephalograms with model-based fixed classifiers, but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, operation of a BCI based on a de novo classifier with a fixed decision boundary based on biologically unnatural features, deformed the neural manifold to be orthogonal to the boundary. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.

scholarly journals Distinct types of neural reorganization during long-term learning

2019 ◽  
Vol 121 (4) ◽  
pp. 1329-1341 ◽  
Author(s):  
Xiao Zhou ◽  
Rex N. Tien ◽  
Sadhana Ravikumar ◽  
Steven M. Chase
Keyword(s):  
Neural Activity ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Skill Learning ◽  
Cortical Reorganization ◽  
Computer Interface ◽  
Slow Phase ◽  
Full Time ◽  
Motor Error

What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW & NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.

scholarly journals Constraints on neural redundancy

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Jay A Hennig ◽  
Matthew D Golub ◽  
Peter J Lund ◽  
Patrick T Sadtler ◽  
Emily R Oby ◽  
...  
Keyword(s):  
Neural Activity ◽  
Rhesus Monkeys ◽  
Primary Motor Cortex ◽  
Activity Patterns ◽  
Computer Interface ◽  
Neural Population ◽  
Population Activity ◽  
The Brain ◽  
Selection Of ◽  
And Task

Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.

scholarly journals Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Laura Carelli ◽  
Federica Solca ◽  
Andrea Faini ◽  
Paolo Meriggi ◽  
Davide Sangalli ◽  
...  
Keyword(s):  
Neural Plasticity ◽  
Cognitive Assessment ◽  
Developmental Disorders ◽  
Neurological Diseases ◽  
Brain Computer Interface ◽  
Autism Spectrum ◽  
Computer Interface ◽  
Elderly Subjects ◽  
Severe Motor ◽  
And Training

Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients’ motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes.

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