Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex

2015 ◽  
Author(s):  
Nir Even-Chen ◽  
Sergey D. Stavisky ◽  
Jonathan C. Kao ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy
Keyword(s):  
Motor Cortex ◽  
Neural Activity ◽  
Error Detection ◽  
Brain Machine Interface ◽  
Machine Interface

scholarly journals Population subspaces reflect movement intention for arm and brain-machine interface control

2019 ◽  
Author(s):  
H. Lalazar ◽  
J.M. Murray ◽  
L.F. Abbott ◽  
E. Vaadia
Keyword(s):  
Motor Cortex ◽  
Neural Activity ◽  
Primary Motor Cortex ◽  
Action Observation ◽  
Neuronal Population ◽  
Brain Machine Interface ◽  
Population Activity ◽  
Interface Control ◽  
Machine Interface ◽  
Movement Intention

Motor cortex is active during covert motor acts, such as action observation and mental rehearsal, when muscles are quiescent. Such neuronal activity, which is thought to be similar to the activity underlying overt movement, is exploited by neural prosthetics to afford subjects control of an external effector. We compared neural activity in primary motor cortex of monkeys who controlled a cursor using either their arm or a brain-machine interface (BMI) to identify what features of neural activity are similar or dissimilar in these two control contexts. Neuronal population activity parcellates into orthogonal subspaces, with some representations that are unique to arm movements and others that are shared between arm and BMI control. The shared subspace is invariant to the effector used and to biomechanical details of the movement, revealing a representation that reflects movement intention. This intention representation is likely the signal extracted by BMI algorithms for cursor control, and subspace orthogonality accounts for how neurons involved in arm control can drive a BMI while the arm remains at rest. These results provide a resolution to the long-standing debate of whether motor cortex represents muscle activity or abstract movement variables, and it clarifies various puzzling aspects of neural prosthetic research.

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.

scholarly journals Rate-Based Decoding of Images from Multi-Region, High Resolution, Electrode Recordings In the Mouse Visual System

2021 ◽  
Author(s):  
Christopher B Fritz
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Neural Activity ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Brain Machine Interface ◽  
Feed Forward ◽  
Deep Networks ◽  
Feed Forward Neural Networks ◽  
Machine Interface

We hypothesize that deep networks are superior to linear decoders at recovering visual stimuli from neural activity. Using high-resolution, multielectrode Neuropixels recordings, we verify this is the case for a simple feed-forward deep neural network having just 7 layers. These results suggest that these feed-forward neural networks and perhaps more complex deep architectures will give superior performance in a visual brain-machine interface.

scholarly journals Reward Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

2018 ◽  
Author(s):  
Junmo An ◽  
Taruna Yadav ◽  
John P. Hessburg ◽  
Joseph T. Francis
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Machine Interface ◽  
Field Potentials ◽  
Alpha Oscillations ◽  
Firing Rates ◽  
Neural Spiking ◽  
Machine Interface ◽  
Field Coherence ◽  
Spike Firing

ABSTRACTReward modulation of the primary motor cortex (M1) could be exploited in developing an autonomously updating brain-machine interface (BMI) based on a reinforcement learning architecture. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and singleunit/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while five non-human primates performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of alpha (8-14 Hz) power, alpha-gamma comodulation, alpha spike-field coherence, and firing rates in general in M1. Furthermore, we found that an increase in alpha-band power was correlated with a decrease in neural spiking activity, that firing rates were highest at the trough of the alpha-band cycle and lowest at the peak of its cycle. These findings imply that alpha oscillations modulated by reward expectation have an influence on spike firing rate and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BMI decoding (An et al., 2018; Zhao et al., 2018).Significance StatementKnowing the subjective value of performed or observed actions is valuable feedback that can be used to improve the performance of an autonomously updating brain-machine interface (BMI). Reward-related information in the primary motor cortex (M1) may be crucial for more stable and robust BMI decoding (Zhao et al., 2018). Here, we present how expectation of reward during motor tasks, or simple observation, is represented by increased spike firing rates in conjunction with decreased alpha (8-14 Hz) oscillatory power, alpha-gamma comodulation, and alpha spike-field coherence, as compared to non-rewarding trials. Moreover, a phasic relation between alpha oscillations and firing rates was observed where firing rates were found to be lowest and highest at the peak and trough of alpha oscillations, respectively.

scholarly journals Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface

2020 ◽  
Author(s):  
Samuel R. Nason ◽  
Matthew J. Mender ◽  
Alex K. Vaskov ◽  
Matthew S. Willsey ◽  
Parag G. Patil ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Cortical Neurons ◽  
High Speed ◽  
Neural Prostheses ◽  
Brain Machine Interface ◽  
Brain Machine Interfaces ◽  
Individual Finger ◽  
Machine Interface ◽  
Group Movements

SUMMARYModern brain-machine interfaces can return function to people with paralysis, but current hand neural prostheses are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a novel task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined, presenting separate targets for each group. During online brain control, the ReFIT Kalman filter demonstrated the capability of individuating movements of each finger group with high performance, enabling a nonhuman primate to acquire two targets simultaneously at 1.95 targets per second, resulting in an average information throughput of 2.1 bits per second. To understand this result, we performed single unit tuning analyses. Cortical neurons were active for movements of an individual finger group, combined movements of both finger groups, or both. Linear combinations of neural activity representing individual finger group movements predicted the neural activity during combined finger group movements with high accuracy, and vice versa. Hence, a linear model was able to explain how cortical neurons encode information about multiple dimensions of movement simultaneously. Additionally, training ridge regressing decoders with independent component movements was sufficient to predict untrained higher-complexity movements. Our results suggest that linear decoders for brain-machine interfaces may be sufficient to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

scholarly journals Emergent coordination with a brain–machine interface: implications for the neural basis of motor learning

2018 ◽  
Vol 120 (3) ◽  
pp. 889-892
Author(s):  
Madhur Mangalam
Keyword(s):  
Motor Learning ◽  
Neural Activity ◽  
Motor Behavior ◽  
Motor Output ◽  
Brain Machine Interface ◽  
Neural Basis ◽  
Reach To Grasp ◽  
Machine Interface ◽  
Emergent Coordination

How patterns of covariance in motor output and neural activity emerge over the course of learning is a topic of ongoing investigation. Vaidya et al. (Vaidya M, Balasubramanian K, Southerland J, Badreldin I, Eleryan A, Shattuck K, Gururangan S, Slutzky M, Osborne L, Fagg A, Oweiss K, Hatsopoulos NG. J Neurophysiol 119: 1291–1304, 2018) investigate the emergence of patterns of covariance in the motor output and neural activity in chronically amputated macaques learning reach-to-grasp movements with a brain–machine interface. The authors’ findings have implications for uncovering general principles of how neural coordination unfolds while learning a different motor behavior.

scholarly journals The brain uses invariant dynamics to generalize outputs across movements

2021 ◽  
Author(s):  
Vivek R. Athalye ◽  
Preeya Khanna ◽  
Suraj Gowda ◽  
Amy L. Orsborn ◽  
Rui M. Costa ◽  
...  
Keyword(s):  
Optimal Control ◽  
Nervous System ◽  
Neural Activity ◽  
Optimal Control Theory ◽  
Activity Patterns ◽  
Network Connectivity ◽  
Brain Machine Interface ◽  
Recent Proposal ◽  
Machine Interface ◽  
The Brain

AbstractThe nervous system uses a repertoire of outputs to produce diverse movements. Thus, the brain must solve how to issue and transition the same outputs in different movements. A recent proposal states that network connectivity constrains the transitions of neural activity to follow invariant rules across different movements, which we term ‘invariant dynamics’. However, it is unknown whether invariant dynamics are actually used to drive and generalize outputs across movements, and what advantage they provide for controlling movement. Using a brain-machine interface that transformed motor cortex activity into outputs for a neuroprosthetic cursor, we discovered that the same output is issued by different activity patterns in different movements. These distinct patterns then transition according to a model of invariant dynamics, leading to patterns that drive distinct future outputs. Optimal control theory revealed this use of invariant dynamics reduces the feedback input needed to control movement. Our results demonstrate that the brain uses invariant dynamics to generalize outputs across movements.

scholarly journals Emergent coordination underlying learning to reach to grasp with a brain-machine interface

2018 ◽  
Vol 119 (4) ◽  
pp. 1291-1304 ◽  
Author(s):  
Mukta Vaidya ◽  
Karthikeyan Balasubramanian ◽  
Joshua Southerland ◽  
Islam Badreldin ◽  
Ahmed Eleryan ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Operant Conditioning ◽  
Neural Plasticity ◽  
Rhesus Macaques ◽  
Robotic Arm ◽  
Brain Machine Interface ◽  
Reach To Grasp ◽  
Cross Covariance ◽  
Machine Interface ◽  
Highly Correlated

The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural plasticity in M1 associated with learning to use a brain-machine interface.

Sign in / Sign up

Export Citation Format

Share Document