Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, 150 Kilgour Rd., Toronto, Ontario, Canada.
Neuroimage. 2013 Aug 15;77:186-94. doi: 10.1016/j.neuroimage.2013.03.028. Epub 2013 Mar 26.
Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising method of improving the accuracy of future brain-computer interfaces.
先前的脑机接口（BCI）研究主要集中在单一的神经影像学模式，如近红外光谱（NIRS）或经颅多普勒超声（TCD）。然而，多模态脑机接口结合了来自不同脑模式的信号，被认为是提高 BCI 系统准确性的一种潜在方法。在本文中，我们比较了仅使用 NIRS 信号、仅使用 TCD 信号以及 NIRS 和 TCD 信号组合所能达到的分类精度。招募了 9 名健康受试者（平均年龄=25.7），在块刺激范式的背景下，当参与者被提示执行言语流畅性任务或保持静止时，同时使用 NIRS 和 TCD 仪器进行测量。使用线性判别分析，使用 NIRS、TCD 和 NIRS-TCD 系统分别将言语流畅性任务分类为 76.1±9.9%、79.4±10.3%和 86.5±6.0%。在 9 名参与者中的 5 名中，NIRS-TCD 系统的分类精度显著高于 NIRS 或 TCD 系统（p<0.05）。我们的结果表明，多模态神经影像学可能是提高未来脑机接口准确性的一种有前途的方法。
