Abdalmalak Androu, Milej Daniel, Yip Lawrence C M, Khan Ali R, Diop Mamadou, Owen Adrian M, St Lawrence Keith
Department of Medical Biophysics, Western University, London, ON, Canada.
Imaging Program, Lawson Health Research Institute, London, ON, Canada.
Front Neurosci. 2020 Feb 18;14:105. doi: 10.3389/fnins.2020.00105. eCollection 2020.
Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.
脑机接口（BCIs）作为一种改善残疾患者生活质量的工具正变得越来越受欢迎。最近，基于时间分辨功能近红外光谱（TR-fNIRS）的脑机接口因其增强的深度敏感性而受到关注，这导致来自脑外层的信号污染更低。本研究首次报道了基于TR-fNIRS的脑机接口在健康参与者身上用于“心理交流”的情况。招募了21名参与者，并反复向他们提出一系列问题，要求他们在回答“是”时想象打网球，在回答“否”时保持放松。由于光子平均飞行时间的变化在深度敏感性和信噪比之间提供了良好的折衷，因此被用于计算氧合血红蛋白和脱氧血红蛋白浓度的变化。从平均氧合血红蛋白信号中提取特征，将其分类为“是”或“否”的回答。使用线性判别分析（LDA）和支持向量机（SVM）分类器，采用留一法交叉验证方法对回答进行分类。使用LDA和SVM时，所有参与者的总体准确率分别为75%和76%。结果还表明，不同问题之间的准确率没有显著差异。此外，在21名参与者中的7名参与者进行运动想象（MI）和休息期间记录了生理参数[心率（HR）和平均动脉压（MAP）]，以研究这些参数在不同条件之间的变化。在不同条件之间未发现这些参数有显著差异。这些发现表明，TR-fNIRS可能适合作为脑损伤患者的脑机接口。
