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

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 Single-trial connectivity estimation for classification of motor imagery data

2013 ◽  
Vol 10 (4) ◽  
pp. 046006 ◽  
Author(s):  
Martin Billinger ◽  
Clemens Brunner ◽  
Gernot R Müller-Putz
Keyword(s):  
Motor Imagery ◽  
Single Trial ◽  
Imagery Data
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