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  Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Vidaurre, C., Ramos-Murguialday, A., Haufe, S., Gómez-Fernández, M., Müller, K.-R., & Nikulin, V. V. (2019). Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation. NeuroImage, 199, 375-386. doi:10.1016/j.neuroimage.2019.05.074.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-B3A7-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-5BC1-C
Genre: Journal Article

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 Creators:
Vidaurre, C.1, 2, Author
Ramos-Murguialday, A.3, 4, Author
Haufe, S.5, Author
Gómez-Fernández, M.1, Author
Müller, K.-R.2, 6, 7, Author
Nikulin, Vadim V.8, 9, Author              
Affiliations:
1Statistics, Informatics and Mathematics Department, Public University of Navarre, Spain, ou_persistent22              
2Department of Machine Learning, TU Berlin, Germany, ou_persistent22              
3Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls University Tübingen, Germany, ou_persistent22              
4Neurotechnology, TECNALIA Health, San Sebastian, Spain, ou_persistent22              
5Berlin Center for Advanced Neuroimaging (BCAN), Charité University Medicine Berlin, Germany, ou_persistent22              
6Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              
7Max Planck Institute for Informatics, Saarbrücken, Germany, ou_persistent22              
8Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
9Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              

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Free keywords: Motor imagery (MI); Sensory threshold neuromuscular electrical stimulation (STM); Afferent patterns; Efferent patterns; Brain-computer interfacing (BCI) inefficiency
 Abstract: An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) STM - when performing motor imagery involving a stimulated joint (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).

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Language(s): eng - English
 Dates: 2019-05-082019-02-012019-05-282019-06-012019-10-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2019.05.074
PMID: 31158476
Other: Epub ahead of print
 Degree: -

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Project name : -
Grant ID : 01IS14013A-E ; 01GQ1115
Funding program : -
Funding organization : German Ministry for Education and Research (BMBF)
Project name : -
Grant ID : MU 987/19-1 ; MU987/14-1 ; MU 987/3-2
Funding program : -
Funding organization : Deutsche Forschungsgesellschaft (DFG)
Project name : MUltimodal Neuroprostesis for Daily Upper limb Support / MUNDUS
Grant ID : 248326
Funding program : Funding Programme 7
Funding organization : European Commission (EC)
Project name : -
Grant ID : 01IS14013A
Funding program : -
Funding organization : German Ministry for Education and Research as Berlin Big Data Centre
Project name : -
Grant ID : 01IS18037I
Funding program : -
Funding organization : Berlin Center for Machine Learning
Project name : EXC 2046/1
Grant ID : -
Funding program : -
Funding organization : Deutsche Forschungsgesellschaft (DFG)
Project name : -
Grant ID : 2017-0-00451 ; 2017-0-01779
Funding program : Institute for Information & Communications Technology Planning & Evaluation (IITP) grant
Funding organization : Korea government
Project name : -
Grant ID : RYC-2014-15671
Funding program : -
Funding organization : Spanish Ministry of Economy
Project name : -
Grant ID : -
Funding program : Russian Academic Excellence Project 5–100
Funding organization : Ministry of Science and Higher Education of the Russian Federation

Source 1

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Title: NeuroImage
Source Genre: Journal
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Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 199 Sequence Number: - Start / End Page: 375 - 386 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166