scholarly journals The Sensitivity of Single-Trial Mu-Suppression Detection for Motor Imagery Performance as Compared to Motor Execution and Motor Observation Performance

2019 ◽  
Vol 13 ◽  
Author(s):  
Kunyu Xu ◽  
Yu-Yu Huang ◽  
Jeng-Ren Duann
Keyword(s):  
Motor Imagery ◽  
Motor Execution ◽  
Single Trial ◽  
Mu Suppression

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.

Feature combination for classifying single-trial ECoG during motor imagery of different sessions

2007 ◽  
Vol 17 (7) ◽  
pp. 851-858 ◽  
Author(s):  
Wei Quigguo ◽  
Meng Fei ◽  
Wang Yijun ◽  
Gao Xiaorong ◽  
Goa Shangkai
Keyword(s):  
Motor Imagery ◽  
Feature Combination ◽  
Single Trial

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

Recognizing single-trial motor imagery EEG based on interpretable clustering method

2021 ◽  
Vol 63 ◽  
pp. 102171
Author(s):  
Rongrong Fu ◽  
Weishuai Li ◽  
Junxiang Chen ◽  
Mengmeng Han
Keyword(s):  
Motor Imagery ◽  
Single Trial ◽  
Clustering Method
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