Aghajani Haleh, Garbey Marc, Omurtag Ahmet
Department of Biomedical Engineering, University of HoustonHouston, TX, United States.
Center for Computational Surgery, Department of Surgery, Research Institute, Houston MethodistHouston, TX, United States.
Front Hum Neurosci. 2017 Jul 14;11:359. doi: 10.3389/fnhum.2017.00359. eCollection 2017.
We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter -back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.
我们研究了一种混合功能性神经成像技术量化人类心理负荷（MWL）的能力。我们使用脑电图（EEG）和功能性近红外光谱（fNIRS）作为成像方式，让17名健康受试者执行字母回读任务，这是一种与工作记忆（WM）相关的标准实验范式。通过将从0到3进行变化，MWL水平发生参数性改变。19个EEG通道覆盖整个头部，19个fNIRS通道位于前额以覆盖参与WM的最主要脑区。对记录信号进行总体组块平均揭示了MWL水平变化期间氧合血红蛋白水平的特定行为。一种机器学习方法已被用于检测MWL水平。我们从EEG、fNIRS以及EEG + fNIRS信号中提取不同特征作为MWL的生物标志物，并将它们作为训练集和测试集输入到线性支持向量机（SVM）中。根据文献，这些特征是基于它们对MWL水平变化的敏感性来选择的。我们在fNIRS和EEG + fNIRS系统中引入了一类新的特征。此外，系统地评估了每个特征类别的性能水平。我们还评估了特征数量和窗口大小对分类性能的影响。使用SVM分类器来区分来自二分类和多分类状态的不同认知状态组合。除了分类器的交叉验证性能水平外，还计算了其他指标，如敏感性、特异性和预测值，以对分类系统进行全面评估。混合（EEG + fNIRS）系统的准确率显著高于EEG或fNIRS单独使用时的准确率。我们的结果表明，EEG + fNIRS特征与分类器相结合能够稳健地区分不同水平的MWL。结果表明，在开发被动脑机接口和其他需要监测用户MWL的应用中，EEG + fNIRS应优于仅使用EEG或fNIRS。
