Human-Robot Interaction Laboratory, Department of Computer Science, Tufts University Medford, MA, USA.
Front Neurosci. 2014 May 23;8:117. doi: 10.3389/fnins.2014.00117. eCollection 2014.
Functional near infrared spectroscopy (NIRS) is a relatively new technique complimentary to EEG for the development of brain-computer interfaces (BCIs). NIRS-based systems for detecting various cognitive and affective states such as mental and emotional stress have already been demonstrated in a range of adaptive human-computer interaction (HCI) applications. However, before NIRS-BCIs can be used reliably in realistic HCI settings, substantial challenges oncerning signal processing and modeling must be addressed. Although many of those challenges have been identified previously, the solutions to overcome them remain scant. In this paper, we first review what can be currently done with NIRS, specifically, NIRS-based approaches to measuring cognitive and affective user states as well as demonstrations of passive NIRS-BCIs. We then discuss some of the primary challenges these systems would face if deployed in more realistic settings, including detection latencies and motion artifacts. Lastly, we investigate the effects of some of these challenges on signal reliability via a quantitative comparison of three NIRS models. The hope is that this paper will actively engage researchers to acilitate the advancement of NIRS as a more robust and useful tool to the BCI community.
功能性近红外光谱（NIRS）是一种相对较新的技术，可与 EEG 互补，用于开发脑机接口（BCI）。已经在一系列自适应人机交互（HCI）应用中展示了基于 NIRS 的系统，用于检测各种认知和情感状态，例如精神和情绪压力。然而，在 NIRS-BCI 可以在现实的 HCI 设置中可靠地使用之前，必须解决关于信号处理和建模的重大挑战。尽管以前已经确定了许多此类挑战，但克服这些挑战的解决方案仍然很少。在本文中，我们首先回顾一下当前可以使用 NIRS 完成的工作，特别是基于 NIRS 的测量认知和情感用户状态的方法以及被动 NIRS-BCI 的演示。然后，我们讨论了如果在更现实的环境中部署这些系统将面临的一些主要挑战，包括检测延迟和运动伪影。最后，我们通过对三种 NIRS 模型的定量比较来研究这些挑战对信号可靠性的影响。希望本文能够积极吸引研究人员，促进 NIRS 作为一种更强大和有用的工具向 BCI 社区的发展。
