An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach
An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach
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DOI：
10.1109/tnsre.2020.3038717
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发表时间：
2020-12-01
影响因子：
4.9
通讯作者：
Shahriari, Y.
中科院分区：
文献类型：
--
作者：
Hosni, S. M.;Borgheai, S. B.;Shahriari, Y.
目的：功能近红外光谱（fNIRS）最近在基于运动想象（MI）的脑机接口（BCI）研究中获得了动力。然而，引人注目的是，大部分研究工作主要致力于为健康个体增强基于fNIRS的BCI。肌萎缩侧索硬化症（ALS）患者的能力，在主要的BCI终端用户利用fNIRS为基础的血流动力学反应，有效地控制MI为基础的BCI，尚未被探讨。本研究旨在量化ALS患者对MI任务的血流动力学反应的受试者特异性时空特征，并研究使用这些反应作为通信手段来控制二元BCI的可行性。方法：使用fNIRS记录8例ALS患者在执行MI-Rest任务时的血流动力学反应。广义线性模型（GLM）分析进行统计学估计和评估个性化的空间激活。选择的通道集进行统计优化分类。受试者特定的判别功能，包括建议的数据驱动的估计系数从GLM，和优化的分类参数进行了识别，并用于进一步评估使用线性支持向量机（SVM）分类器的性能。结果：患者血流动力学反应的时空特征存在个体间差异。使用优化的分类参数和特征集，所有受试者都可以成功地使用他们的MI血流动力学反应来控制BCI，平均分类准确率为85.4% +/-9.8%。意义：我们的研究结果表明，一个有前途的应用fNIRS为基础的MI血流动力学反应，以控制二进制BCI ALS患者。这些研究结果突出了特定于主题的数据驱动的方法，以确定歧视性的时空特征，优化BCI性能的重要性。
Objective: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum in research on motor-imagery (MI)-based brain-computer interfaces (BCIs). However, strikingly, most of the research effort is primarily devoted to enhancing fNIRS-based BCIs for healthy individuals. The ability of patients with amyotrophic lateral sclerosis (ALS), among the main BCI end-users to utilize fNIRS-based hemodynamic responses to efficiently control an MI-based BCI, has not yet been explored. This study aims to quantify subject-specific spatio-temporal characteristics of ALS patients' hemodynamic responses to MI tasks, and to investigate the feasibility of using these responses as a means of communication to control a binary BCI. Methods: Hemodynamic responses were recorded using fNIRS from eight patients with ALS while performing MI-Rest tasks. The generalized linear model (GLM) analysis was conducted to statistically estimate and evaluate individualized spatial activation. Selected channel sets were statistically optimized for classification. Subject-specific discriminative features, including a proposed data-driven estimated coefficient obtained from GLM, and optimized classification parameters were identified and used to further evaluate the performance using a linear support vector machine (SVM) classifier. Results: Inter-subject variations were observed in spatio-temporal characteristics of patients' hemodynamic responses. Using optimized classification parameters and feature sets, all subjects could successfully use their MI hemodynamic responses to control a BCI with an average classification accuracy of 85.4% +/- 9.8%. Significance: Our results indicate a promising application of fNIRS-based MI hemodynamic responses to control a binary BCI by ALS patients. These findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.