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首页> 外文期刊>IEEE transactions on neural systems and rehabilitation engineering >An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach
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An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach

机译:基于FNIRS的电机图像BCI用于ALS:特定于学科的数据驱动方法

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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.
机译:目的:功能近红外光谱(Fnirs)最近获得了对基于机动图像(MI)的脑电电脑界面(BCI)的研究中的动力。然而,令人惊讶的是,大多数研究努力主要致力于增强基于FNIRS的BCIS的健康个体。尚未探讨患者患有肌营养的外侧硬化症(ALS)的患者患有肌营养的外侧硬化剂(ALS),以利用基于FNIR的血液动力学反应,以有效控制基于MI的BCI。本研究旨在量化ALS患者血流动力学对MI任务的血液动力学响应的特异性时空特征,并研究使用这些响应作为控制二元BCI的通信手段的可行性。方法:使用八个患者的FNIR记录血流动力学响应,同时执行MI休息任务。在统计上估计和评估个体化的空间激活中进行广义线性模型(GLM)分析。所选频道集在统计上优化分类。特定于主题特定的鉴别特征,包括从GLM获得的所提出的数据驱动的估计系数,并识别了优化的分类参数,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:在患者血流动力学反应的时空特征中观察到尿道间变异。使用优化的分类参数和功能集,所有科目都可以成功地使用其MI血液动力学响应来控制BCI,平均分类精度为85.4%+/- 9.8%。意义:我们的结果表明,基于FNIR的MI血液动力学反应的有望应用,以通过ALS患者对二元BCI进行控制。这些发现突出了主题特定的数据驱动方法的重要性,以确定用于优化的BCI性能的判别歧视性时空特征。

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