Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15213, USA.
Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara St, Pittsburgh, PA 15213, USA.
J Neurosci Methods. 2018 Jan 1;293:174-182. doi: 10.1016/j.jneumeth.2017.10.003. Epub 2017 Oct 7.
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.
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.
The experimental results showed that within approximately 3s 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 2.24s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68s.
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.
These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI.
功能 transcranial Doppler（fTCD）是一种基于超声的神经影像学技术，用于通过测量脑血流速度来评估认知任务期间发生的神经激活。
本文的目的是研究基于左、右大脑中动脉血流速度的实时 2 类和 3 类 BCI 的可行性，以响应心理旋转和词汇生成任务。从 fTCD 信号中提取基于五级小波分解的统计特征。采用 Wilcoxon 检验和支持向量机（SVM），具有线性核，用于特征降维和分类。
实验结果表明，在认知任务开始后约 3s 内，心理旋转与静息状态和词汇生成与静息状态的平均准确率分别为 80.29%和 82.35%。在认知任务开始后 2.24s 内，心理旋转任务与词汇生成任务的平均准确率达到 79.72%。此外，在 4.68s 内，3 类问题的平均准确率为 65.27%。
与文献中基于 fTCD 的相关系统相比，这里呈现的结果在准确性和速度方面有显著的提高。具体而言，本手稿中报告的速度比任何现有的二进制和 3 类基于 fTCD 的 BCI 至少快 12 倍和 2.5 倍。
这些结果表明 fTCD 是一种很有前途和可行的候选方法，可用于开发实时 BCI。
