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

Published on Jun 1, 2019in NeuroImage5.81
· DOI :10.1016/j.neuroimage.2019.05.074
Carmen Vidaurre21
Estimated H-index: 21
(Technical University of Berlin),
A. Ramos Murguialday1
Estimated H-index: 1
(University of Tübingen)
+ 3 AuthorsVadim V. Nikulin29
Estimated H-index: 29
(Charité)
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Abstract
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) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (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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References58
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#1Claudia Sannelli (Technical University of Berlin)H-Index: 13
#2Carmen Vidaurre (Technical University of Berlin)H-Index: 21
Last.Benjamin Blankertz (Technical University of Berlin)H-Index: 53
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#1Xiaokang Shu (SJTU: Shanghai Jiao Tong University)H-Index: 2
#2Lin Yao (UW: University of Waterloo)H-Index: 6
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
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#1Adelyn P. Tu-Chan (UCSF: University of California, San Francisco)H-Index: 1
#2Nikhilesh Natraj (UCSF: University of California, San Francisco)H-Index: 3
Last.Karunesh Ganguly (UCSF: University of California, San Francisco)H-Index: 17
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#1Andrea Serino (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 32
#2Michel Akselrod (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 2
Last.François Lüthi (Southwest University of Visual Arts)H-Index: 14
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#1Mahnaz Arvaneh (University of Sheffield)H-Index: 8
#2Cuntai Guan (Agency for Science, Technology and Research)H-Index: 38
Last.Chuanchu Wang (Agency for Science, Technology and Research)H-Index: 18
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#1Jan Saputra Müller (Technical University of Berlin)H-Index: 3
#2Carmen Vidaurre (Technical University of Berlin)H-Index: 21
Last.Klaus-Robert Müller (Technical University of Berlin)H-Index: 82
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#1Keyvan Mahjoory (Technical University of Berlin)H-Index: 1
#2Vadim V. Nikulin (Charité)H-Index: 29
Last.Stefan Haufe (Technical University of Berlin)H-Index: 24
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