scholarly journals Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor representations

2017 ◽  
Author(s):  
Yao Zhao ◽  
John P. Hessburg ◽  
Jaganth Nivas Asok Kumar ◽  
Joseph Thachil Francis
Keyword(s):  
Paradigm Shift ◽  
Primary Motor Cortex ◽  
Brain Machine Interface ◽  
Active Movement ◽  
Gating System ◽  
Interface Control ◽  
Control Research ◽  
Machine Interface ◽  
Tuning Function ◽  
Sensorimotor Representations

AbstractNeural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe will lead to a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1, and the primary somatosensory cortex (S1), are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance

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.

scholarly journals Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0131491 ◽  
Author(s):  
Iñaki Iturrate ◽  
Jonathan Grizou ◽  
Jason Omedes ◽  
Pierre-Yves Oudeyer ◽  
Manuel Lopes ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Task Constraints ◽  
Interface Control ◽  
Related Potentials ◽  
Machine Interface

scholarly journals Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Neuron ◽  
2019 ◽  
Vol 102 (3) ◽  
pp. 694-705.e3 ◽  
Author(s):  
Sofia Sakellaridi ◽  
Vassilios N. Christopoulos ◽  
Tyson Aflalo ◽  
Kelsie W. Pejsa ◽  
Emily R. Rosario ◽  
...  
Keyword(s):  
Brain Machine Interface ◽  
Interface Control ◽  
Machine Interface ◽  
Intrinsic Variable

Brain-Machine Interface Control via Reinforcement Learning

2007 ◽  
Author(s):  
Jack DiGiovanna ◽  
Babak Mahmoudi ◽  
Jeremiah Mitzelfelt ◽  
Justin C. Sanchez ◽  
Jose C. Principe
Keyword(s):  
Reinforcement Learning ◽  
Brain Machine Interface ◽  
Interface Control ◽  
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 Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

2011 ◽  
Vol 106 (2) ◽  
pp. 564-575 ◽  
Author(s):  
A. Cherian ◽  
M. O. Krucoff ◽  
L. E. Miller
Keyword(s):  
Primary Motor Cortex ◽  
Brain Machine Interface ◽  
Stable Relationship ◽  
End Point ◽  
Movement Dynamics ◽  
Point Forces ◽  
Limb Kinematics ◽  
Machine Interface ◽  
Kinetics Of ◽  
Kinematics And Kinetics

During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.

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