A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines
A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines
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DOI：
10.1016/j.jneumeth.2017.10.003
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发表时间：
2018-01-01
影响因子：
3
通讯作者：
Akcakaya, Murat
中科院分区：
文献类型：
--
作者：
Khalaf, Aya;Sybeldon, Matthew;Akcakaya, Murat
背景：功能性经颅多普勒（fTCD）是一种基于超声的神经成像技术，用于通过测量脑血流速度来评估认知任务中发生的神经激活。新方法：研究基于大脑左右中动脉血流速度的2级和3级实时脑机接口（BCI）在心理旋转和单词生成任务中的可行性。基于五阶小波分解提取fTCD信号的统计特征。采用线性核的Wilcoxon测试和支持向量机（SVM）进行特征约简和分类。结果：实验结果表明，在认知任务开始后约3 s内，心理旋转与静息状态的平均正确率为80.29%，单词生成与静息状态的平均正确率为82.35%。在认知任务开始后的2.24 s内，心理旋转任务和词生成任务的平均正确率达到79.72%。在4.68 s内，对3类问题的平均准确率达到65.27%。与现有方法的比较：与文献中基于ftcd的相关系统相比，本文的结果在准确性和速度方面有了显著的提高。具体来说，本文中报告的速度分别比任何现有的基于二进制和3级ftcd的bci快至少12倍和2.5倍。结论：这些结果表明，fTCD是一种有希望和可行的候选物，可用于开发实时脑机接口。Elsevier B.V.出版
Background: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow.New method: The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals. The Wilcoxon test and support vector machines (SVM), with a linear kernel, were employed for feature reduction and classification.Results: The experimental results showed that within approximately 3 s of the onset of the cognitive task average accuracies of 80.29%, and 82.35% were obtained for the mental rotation versus resting state and the word generation versus resting state respectively. The mental rotation task versus word generation task achieved an average accuracy of 79.72% within 2.24 s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68 s.Comparison with existing methods: The results presented here provide significant improvement compared to the relevant fTCD-based systems presented in literature in terms of accuracy and speed. Specifically, the reported speed in this manuscript is at least 12 and 2.5 times faster than any existing binary and 3-class fTCD-based BCIs, respectively.Conclusions: These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI. Published by Elsevier B.V.