Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement.
Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement.
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DOI：
10.1007/s00521-016-2234-7
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发表时间：
2017
影响因子：
6
通讯作者：
Wang C
中科院分区：
文献类型：
--
作者：
Arvaneh M;Guan C;Ang KK;Ward TE;Chua KSG;Kuah CWK;Ephraim Joseph GJ;Phua KS;Wang C
基于运动图像的脑机接口（MI-BCI）已被提出作为一种康复工具，以促进运动恢复中风。然而，BCI系统的校准对于中风患者来说是一个耗时且疲劳的过程，这使得实际治疗交互的时间减少。研究表明，被动运动（PM）（即，由外部机构执行运动而没有任何自主运动）和运动想象（MI）（即，在没有任何肌肉激活的情况下对运动的心理排练）在运动皮层上诱导类似的EEG模式。由于执行PM对患者来说不太疲劳，因此本文研究了从PM校准MI-BCI对中风受试者在分类准确性方面的有效性。为此，提出了一种新的自适应滤波器组数据空间自适应算法（FB-DSA）。FB-DSA算法对带通滤波后的MI数据进行线性变换，使MI和PM数据之间的分布差异最小化。所提出的算法的有效性进行了评估，从16名健康受试者和6名中风患者收集的数据的离线研究。实验结果表明，FB-DSA算法显著提高了PM和MI校准模型的分类精度（p < 0.05）。根据获得的分类精度，使用所提出的FB-DSA算法适配的PM校准模型对于健康受试者和中风受试者分别平均优于MI校准模型2.3%和4.5%。此外，我们的研究结果表明，MI和PM之间的差距可能是更强的中风患者相比，健康受试者，因此，将有一个增加的需要使用拟议的FB-DSA算法在BCI为基础的中风康复校准PM。
Motor imagery-based brain–computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models (p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM.
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影响因子：
9.8
作者：
Kaplan R;Doeller CF;Barnes GR;Litvak V;Düzel E;Bandettini PA;Burgess N
通讯作者：
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基于联合回归模型和谱功率的运动想象分类
影响因子：
6
作者：
Hu, Sanqing;Tian, Qiangqiang;Kong, Wanzeng
通讯作者：
Kong, Wanzeng