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Tactile Sensation Assisted Motor Imagery Training for Enhanced BCI Performance: A Randomized Controlled Study.
IEEE Trans Biomed Eng ; 70(2): 694-702, 2023 02.
Article em En | MEDLINE | ID: mdl-36001509
OBJECTIVE: Independent of conventional neurofeedback training, in this study, we propose a tactile sensation assisted motor imagery training (SA-MI Training) approach to improve the performance of MI-based BCI. METHODS: Twenty-six subjects were recruited and randomly divided into a Training-Group and a Control-Group. All subjects were required to perform three blocks of MI tasks. In the Training-Group, during the second block (SA-MI Training block), tactile stimulation was applied to the left or right wrist while the subjects were performing the left or right-hand MI task, while during the first block (Pre-Training block) and the third block (Post-Training block), subjects performed pure MI tasks without the tactile sensation assistance. In contrast, in the Control-Group, subjects performed the left and right-hand MI tasks in all three blocks. RESULTS: The performance of the Post-Training block (83.2 ± 11.4%) was significantly (p = 0.0014) higher than that of the Pre-Training block (73.2 ± 16.3%). By contrast, in the Control-Group, no significant difference was found among the three blocks. Moreover, after the SA-MI Training, the motor-related cortex activation (i.e., ERD/ERS) and the R 2 coefficient in the alpha-beta band were enhanced, while no training effect was found in the Control-Group. CONCLUSION: The proposed SA-MI Training approach can significantly improve the performance of MI, which provides a novel training framework for MI-based BCI. SIGNIFICANCE: It may be especially beneficial to those who are with difficulty in convention neurofeedback training or performing pure MI mental tasks to gain BCI control.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Clinical_trials Idioma: En Revista: IEEE Trans Biomed Eng Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Eletroencefalografia / Interfaces Cérebro-Computador Tipo de estudo: Clinical_trials Idioma: En Revista: IEEE Trans Biomed Eng Ano de publicação: 2023 Tipo de documento: Article