scholarly journals Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Seokyun Ryun ◽  
June Sic Kim ◽  
Sang Hun Lee ◽  
Sehyoon Jeong ◽  
Sung-Phil Kim ◽  
...  
Keyword(s):  
Motor Planning ◽  
Elbow Flexion ◽  
Beta Band ◽  
Movement Onset ◽  
Prefrontal Area ◽  
Brain Responses ◽  
Power Difference ◽  
Sensorimotor Area ◽  
Movement Type ◽  
Specific Frequency

Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area’s premovement signals (−2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13–30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.

scholarly journals Beta-band desynchronization reflects uncertainty in effector selection during motor planning

2021 ◽  
Author(s):  
Milou J.L. van Helvert ◽  
Leonie Oostwoud Wijdenes ◽  
Linda Geerligs ◽  
W. Pieter Medendorp
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Motor Planning ◽  
Reaction Times ◽  
Motor Preparation ◽  
Motion Artifact ◽  
Theta Band ◽  
Beta Band ◽  
Target Uncertainty ◽  
Band Power

AbstractWhile beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants (n = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants’ choice, and the target was close to one of the two hands or at about equal distance from both. To measure neural activity in a motion-artifact-free time window, the location of the upcoming target was cued 1000-1500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cueing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice than in the predetermined trials, supporting the notion of a competitive process for hand selection. Modulations of beta-band power over central cortical regions, but not alpha-band or theta-band power, were in line with these observations. During the cueing period, reaches in predetermined trials were preceded by larger decreases in beta-band power than reaches in choice trials. Cue direction did not affect reaction times or beta-band power, which may be due to the cue being invalid in 50% of trials, retaining effector uncertainty during motor planning. Our findings suggest that effector uncertainty, similar to target uncertainty, selectively modulates beta-band power during motor planning.New & NoteworthyWhile reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cueing paradigm, that the power in this frequency band, but not in the alpha or theta-band, is also modulated by the uncertainty of which hand to use. This finding supports the notion that multiple possible effector-specific actions can be specified in parallel up to the level of motor preparation.

scholarly journals Beta-Band Activity during Motor Planning Reflects Response Uncertainty

2010 ◽  
Vol 30 (34) ◽  
pp. 11270-11277 ◽  
Author(s):  
C. Tzagarakis ◽  
N. F. Ince ◽  
A. C. Leuthold ◽  
G. Pellizzer
Keyword(s):  
Motor Planning ◽  
Response Uncertainty ◽  
Beta Band ◽  
Band Activity

Forming Tool Use Representations: A Neurophysiological Investigation into Tool Exposure

2011 ◽  
Vol 23 (10) ◽  
pp. 2920-2934 ◽  
Author(s):  
John Christopher Mizelle ◽  
Teresa Tang ◽  
Nikta Pirouz ◽  
Lewis A. Wheaton
Keyword(s):  
Learning Strategies ◽  
Tool Use ◽  
Motor Planning ◽  
Hemispheric Dominance ◽  
Neural Activation ◽  
The Novel ◽  
Beta Band ◽  
Related Information ◽  
Direct Exposure ◽  
Indirect Exposure

Prior work has identified a common left parietofrontal network for storage of tool-related information for various tasks. How these representations become established within this network on the basis of different modes of exposure is unclear. Here, healthy subjects engaged in physical practice (direct exposure) with familiar and unfamiliar tools. A separate group of subjects engaged in video-based observation (indirect exposure) of the same tools to understand how these learning strategies create representations. To assess neural mechanisms engaged for pantomime after different modes of exposure, a pantomime task was performed for both tools while recording neural activation with high-density EEG. Motor planning–related neural activation was evaluated using beta band (13–22 Hz) event-related desynchronization. Hemispheric dominance was assessed, and activation maps were generated to understand topography of activations. Comparison of conditions (effects of tool familiarity and tool exposure) was performed with standardized low-resolution brain electromagnetic tomography. Novel tool pantomime following direct exposure resulted in greater activations of bilateral parietofrontal regions. Activations following indirect training varied by tool familiarity; pantomime of the familiar tool showed greater activations in left parietofrontal areas, whereas the novel tool showed greater activations at right temporoparieto-occipital areas. These findings have relevance to the mechanisms for understanding motor-related behaviors involved in new tools that we have little or no experience with and can extend into advancing theories of tool use motor learning.

scholarly journals Patterns of hypermetria and terminal cocontraction during point-to-point movements demonstrate independent action of trajectory and postural controllers

2011 ◽  
Vol 106 (5) ◽  
pp. 2368-2382 ◽  
Author(s):  
Robert A. Scheidt ◽  
Claude Ghez ◽  
Supriya Asnani
Keyword(s):  
Visual Target ◽  
Feedforward Control ◽  
Elbow Flexion ◽  
Control Action ◽  
Reverse Direction ◽  
Movement Onset ◽  
Control Actions ◽  
Common Control ◽  
Point To Point ◽  
The Brain

We examined elbow muscle activities and movement kinematics to determine how subjects combine elementary control actions in performing movements with one and two trajectory segments. In reaching, subjects made a rapid elbow flexion to a visual target before stabilizing the limb with either a low or a higher level of elbow flexor/extensor coactivity (CoA), which was cued by target diameter. Cursor diameter provided real-time biofeedback of actual muscle CoA. In reversing, the limb was to reverse direction within the target and return to the origin with minimal CoA. We previously reported that subjects overshoot the goal when attempting a reversal after first having learned to reach accurately to the same target. Here we test the hypothesis that this hypermetria results because reversals co-opt the initial feedforward control action from the preceding trained reach, thereby failing to account for task-dependent changes in limb impedance induced by differences in flexor/extensor coactivity as the target is acquired (higher in reaching than reversing). Instructed increases in elbow CoA began mid-reach, thus increasing elbow impedance and reducing transient oscillations present in low CoA movments. Flexor EMG alone increased at movement onset. Test reversals incorporated the initial agonist activity of previous reaches but not the increased coactivity at the target, thus leading to overshoot. Moreover, we observed elevated coactivity in reversals upon returning to the origin even though coactivity in reaching was centered at the goal target. These findings refute the idea that the brain necessarily invokes distinct unitary control actions for reaches and reversals made to the same target. Instead, reaches and reversals share a common control action that initiates trajectories toward their target and another later control action that terminates movement and stabilizes the limb about its final resting posture, which differs in the two tasks.

scholarly journals The Degree of Modulation of Beta Band Activity During Motor Planning Is Related to Trait Impulsivity

2019 ◽  
Vol 13 ◽  
Author(s):  
Charidimos Tzagarakis ◽  
Andrew Thompson ◽  
Robert D. Rogers ◽  
Giuseppe Pellizzer
Keyword(s):  
Motor Planning ◽  
Beta Band ◽  
Trait Impulsivity ◽  
Band Activity

scholarly journals Beta band oscillations in motor cortex reflect neural population signals that delay movement onset

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Preeya Khanna ◽  
Jose M Carmena
Keyword(s):  
Motor Cortex ◽  
Neural Activity ◽  
Oscillatory Activity ◽  
Neural Population ◽  
Beta Band ◽  
Neural Basis ◽  
Movement Onset ◽  
Beta Oscillations ◽  
Beta Power ◽  
Oscillatory Power

Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported changes remain elusive. To investigate how changes in population neural activity, beta oscillations, and behavior are linked, we recorded multi-scale neural activity from motor cortex while three macaques performed a novel neurofeedback task. Subjects volitionally brought their beta oscillatory power to an instructed state and subsequently executed an arm reach. Reaches preceded by a reduction in beta power exhibited significantly faster movement onset times than reaches preceded by an increase in beta power. Further, population neural activity was found to shift farther from a movement onset state during beta oscillations that were neurofeedback-induced or naturally occurring during reaching tasks. This finding establishes a population neural basis for slowed movement onset following periods of beta oscillatory activity.

scholarly journals Stopping vs Resting state during motor imagery paradigm

2021 ◽  
Author(s):  
Bastien Orset ◽  
Kyuhwa Lee ◽  
Ricardo Chavarriaga ◽  
Jose del R Millan
Keyword(s):  
Motor Imagery ◽  
Resting State ◽  
Beta Band ◽  
Movement Onset ◽  
Major Drawback ◽  
Non Invasive ◽  
Control Brain ◽  
Motor Actions ◽  
Imagined Movement ◽  
Termination Process

Current non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity (MI). This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. One major drawback of such strategy is that users are not trained to stop their actions. Indeed, the termination process involved in BMI is poorly understood with most of the studies assuming that the end of an MI action is similar to the resting state. Here we hypothesize that the process of stopping MI (MI termination) and resting state are two different processes that should be decoded independently due to the exhibition of different neural pattens. We compared the detection of both states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). Our results shows that both decoders show significant differences in term of performances and latency (N=17 Subjects) with the offset decoder able to detect faster and better MI termination. While studying this difference, we found that the offset decoder is primarily based on the use of features in Beta band which appears earlier. Based on this finding, we also proposed a Random Forrest based decoder which enable to distinguish three classes (MI, MI termination and REST).

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