A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery
Objective. We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance...
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| Published in | Journal of neural engineering Vol. 16; no. 6; pp. 66012 - 66023 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
IOP Publishing
23.10.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1741-2560 1741-2552 1741-2552 |
| DOI | 10.1088/1741-2552/ab377d |
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| Abstract | Objective. We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach. 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance. These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training. |
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| AbstractList | We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively.OBJECTIVEWe proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively.64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD).APPROACH64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD).By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT.MAIN RESULTSBy comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT.These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.SIGNIFICANCEThese findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training. We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training. Objective. We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach. 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance. These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training. |
| Author | Qi, Hongzhi Chen, Long Gu, Bin Zhou, Yijie Xu, Minpeng Ming, Dong He, Feng Wang, Zhongpeng Liu, Shuang |
| Author_xml | – sequence: 1 givenname: Zhongpeng orcidid: 0000-0002-3002-1756 surname: Wang fullname: Wang, Zhongpeng organization: Tianjin University Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin 300072, People's Republic of China – sequence: 2 givenname: Yijie orcidid: 0000-0003-1413-8425 surname: Zhou fullname: Zhou, Yijie organization: Tianjin University Academy of Medical Engineering and Translational Medicine, Tianjin 300072, People's Republic of China – sequence: 3 givenname: Long surname: Chen fullname: Chen, Long email: cagor@tju.edu.cn organization: Tianjin University Academy of Medical Engineering and Translational Medicine, Tianjin 300072, People's Republic of China – sequence: 4 givenname: Bin surname: Gu fullname: Gu, Bin organization: Tianjin University Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin 300072, People's Republic of China – sequence: 5 givenname: Shuang surname: Liu fullname: Liu, Shuang organization: Tianjin University Academy of Medical Engineering and Translational Medicine, Tianjin 300072, People's Republic of China – sequence: 6 givenname: Minpeng surname: Xu fullname: Xu, Minpeng organization: Tianjin International Joint Research Center for Neural Engineering , Tianjin 300072, People's Republic of China – sequence: 7 givenname: Hongzhi surname: Qi fullname: Qi, Hongzhi organization: Tianjin International Joint Research Center for Neural Engineering , Tianjin 300072, People's Republic of China – sequence: 8 givenname: Feng surname: He fullname: He, Feng organization: Tianjin International Joint Research Center for Neural Engineering , Tianjin 300072, People's Republic of China – sequence: 9 givenname: Dong surname: Ming fullname: Ming, Dong email: richardming@tju.edu.cn organization: Tianjin International Joint Research Center for Neural Engineering , Tianjin 300072, People's Republic of China |
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| Snippet | Objective. We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and... We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive... |
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| SubjectTerms | brain-computer interface event-related desynchronization motor imagery neurofeedback training proprioceptive electrical stimulation |
| Title | A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery |
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