IEEE Trans Neural Syst Rehabil Eng. 2014 Mar;22(2):222-9. doi: 10.1109/TNSRE.2013.2292995.
Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.
结合电生理和血流动力学特征是提高基于感觉运动节律（SMR）的脑机接口性能的一种新方法。本研究的目的有两个：一是测试基于脑电图/功能近红外光谱（EEG-fNIRS）SMR 的脑机接口在四肢瘫痪患者中的使用可行性；二是研究运动想象和运动意图在该用户群体中的性能差异。与使用单一模态 EEG 分类器相比，使用 EEG 和 fNIRS 特征进行分类的效果更好，尝试运动的平均分类率为 79%，想象运动的平均分类率为 70%。对于对照组，分别获得了 87%和 79%的分类率，其中“尝试运动”条件被“实际运动”所取代。对于当前基于 EEG 的脑机接口缺乏足够控制的用户，结合 EEG-fNIRS 系统可能特别有益。与仅使用 EEG 以及组合分类器相比，患者组在尝试运动时的平均分类性能明显高于想象运动，这表明当前脑机接口研究的范式发生了转变。
