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An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training

Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly...

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Autores principales: Duan, Xu, Xie, Songyun, Xie, Xinzhou, Obermayer, Klaus, Cui, Yujie, Wang, Zhenzhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215169/
https://www.ncbi.nlm.nih.gov/pubmed/34163337
http://dx.doi.org/10.3389/fnhum.2021.625983
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author Duan, Xu
Xie, Songyun
Xie, Xinzhou
Obermayer, Klaus
Cui, Yujie
Wang, Zhenzhen
author_facet Duan, Xu
Xie, Songyun
Xie, Xinzhou
Obermayer, Klaus
Cui, Yujie
Wang, Zhenzhen
author_sort Duan, Xu
collection PubMed
description Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect.
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spelling pubmed-82151692021-06-22 An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training Duan, Xu Xie, Songyun Xie, Xinzhou Obermayer, Klaus Cui, Yujie Wang, Zhenzhen Front Hum Neurosci Neuroscience Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect. Frontiers Media S.A. 2021-06-07 /pmc/articles/PMC8215169/ /pubmed/34163337 http://dx.doi.org/10.3389/fnhum.2021.625983 Text en Copyright © 2021 Duan, Xie, Xie, Obermayer, Cui and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Duan, Xu
Xie, Songyun
Xie, Xinzhou
Obermayer, Klaus
Cui, Yujie
Wang, Zhenzhen
An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title_full An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title_fullStr An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title_full_unstemmed An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title_short An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
title_sort online data visualization feedback protocol for motor imagery-based bci training
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215169/
https://www.ncbi.nlm.nih.gov/pubmed/34163337
http://dx.doi.org/10.3389/fnhum.2021.625983
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