scholarly journals Errors in Imagined and Executed Typing

Vision ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 66
Author(s):  
Stephan F. Dahm ◽  
Martina Rieger
Keyword(s):  
Motor Imagery ◽  
Error Detection ◽  
Motor Command ◽  
Higher Order ◽  
Internal Models ◽  
Motor Execution ◽  
Action Effects ◽  
Intended Action ◽  
Typing Task

In motor imagery (MI), internal models may predict the action effects. A mismatch between predicted and intended action effects may result in error detection. To compare error detection in MI and motor execution (ME), ten-finger typists and hunt-and-peck typists performed a copy-typing task. Visibility of the screen and visibility of the keyboard were manipulated. Participants reported what type of error occurred and by which sources they detected the error. With covered screen, fewer errors were reported, showing the importance of distal action effects for error detection. With covered screen, the number of reported higher-order planning errors did not significantly differ between MI and ME. However, the number of reported motor command errors was lower in MI than in ME. Hence, only errors that occur in advance to internal modeling are equally observed in MI and ME. MI may require more attention than ME, leaving fewer resources to monitor motor command errors in MI. In comparison to hunt-and-peck typists, ten-finger typists detected more higher-order planning errors by kinesthesis/touch and fewer motor command errors by vision of the keyboard. The use of sources for error detection did not significantly differ between MI and ME, indicating similar mechanisms.

Motor imagery helps updating internal models during microgravity exposure

2022 ◽  
Author(s):  
Dylan Rannaud Monany ◽  
Marie Barbiero ◽  
Florent Lebon ◽  
Jan Babič ◽  
Gunnar Blohm ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Normal Gravity ◽  
Parabolic Flight ◽  
Internal Models ◽  
Mere Exposure ◽  
Control Group ◽  
Motor Execution ◽  
Internal Representations ◽  
Before And After ◽  
Imagined Movement

Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.

scholarly journals Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alkinoos Athanasiou ◽  
Chrysa Lithari ◽  
Konstantina Kalogianni ◽  
Manousos A. Klados ◽  
Panagiotis D. Bamidis
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Sensorimotor Cortex ◽  
Mu Rhythm ◽  
Source Detection ◽  
Motor Execution ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Preliminary Study ◽  
Real Hand

Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas.Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome.Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution.Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.

scholarly journals An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals

2021 ◽  
Vol 11 (11) ◽  
pp. 1393
Author(s):  
Saugat Bhattacharyya ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Motor Imagery ◽  
Error Detection ◽  
Error Control ◽  
Transport Theory ◽  
Optimal Transport ◽  
Detection System ◽  
Single Trial ◽  
Neural Signals ◽  
Error Control Mechanism ◽  
Bci System

 This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it. 

scholarly journals Corrigendum: Aging Changes Effective Connectivity of Motor Networks During Motor Execution and Motor Imagery

2020 ◽  
Vol 12 ◽  
Author(s):  
Li Wang ◽  
Ye Zhang ◽  
Jingna Zhang ◽  
Linqiong Sang ◽  
Pengyue Li ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Effective Connectivity ◽  
Motor Execution

scholarly journals Disentangling motor execution from motor imagery with the phantom limb

Brain ◽  
2012 ◽  
Vol 135 (2) ◽  
pp. 582-595 ◽  
Author(s):  
Estelle Raffin ◽  
Jérémie Mattout ◽  
Karen T. Reilly ◽  
Pascal Giraux
Keyword(s):  
Motor Imagery ◽  
Phantom Limb ◽  
Motor Execution
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