| BCI and multimodal feedback-based attention regulation for lower limb rehabilitation |
| Wang, Jiaxing1,2; Wang, Weiqun1; Hou, Zengguang1,3; Shi,Weiguo1,2; Liang, Xu1,2; Ren, Shixin1,2; Peng, Liang1,2; Zhou, Yanjie1,2
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| 2019
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会议名称 | International Joint Conference on Neural Networks
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会议日期 | 2019-7-14
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会议地点 | Budapest, Hungary
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摘要 | Both motor and cognitive function rehabilitation
benefits can be improved significantly by patients’ active participation. However, post-stroke patients, especially with attentiondeficit disorders, can hardly engage in training for a longer time.
In order to improve patients’ attention focused on the training, an
attention regulation system based on the brain-machine interface
(BCI) and multimodal feedback is proposed for post-stroke
lower limb rehabilitation. First, an interactive speed-tracking
riding game is designed to increase the training challenge and
patients’ neural engagement. The character’s riding speed, which
is synchronized with patients’ actual cycling speed, is displayed
on the screen in real time. And patients’ attention can further be
enhanced when they try their best to track the reference speed
curve. Second, an attention classifier is designed and trained
by using subjects’ EEG signals, which are acquired if they are
tracking the reference speed curve or not. This classifier is finally
applied to monitor subject’s attention. If the subject is recognized
with inadequate attention, sharp voice (auditory feedback) and
red screen (visual feedback) will be given by the designed game
to remind the subject to focus on the training. The contrast
experiment results show that subjects’ performance indicated by
speed tracking accuracy and muscle activation can be improved
significantly by using the attention regulation system. Moreover,
the phenomenon of prominent decrease in theta rhythm and
increase in beta rhythm can be found, which is consistent with
previous research and further validates the feasibility of the
proposed system in attention enhancement.
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七大方向——子方向分类 | 多模态智能
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文献类型 | 会议论文
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条目标识符 | http://ir.ia.ac.cn/handle/173211/44385
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专题 | 复杂系统认知与决策实验室_先进机器人
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通讯作者 | Wang, Weiqun |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation,Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology
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第一作者单位 | 中国科学院自动化研究所
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通讯作者单位 | 中国科学院自动化研究所
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推荐引用方式 GB/T 7714 |
Wang, Jiaxing,Wang, Weiqun,Hou, Zengguang,et al. BCI and multimodal feedback-based attention regulation for lower limb rehabilitation[C],2019.
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