Keshmiri Soheil, Sumioka Hidenobu, Yamazaki Ryuji, Ishiguro Hiroshi
Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International Kyoto, Japan.
Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute InternationalKyoto, Japan; The Graduate School of Engineering Science, Osaka UniversityOsaka, Japan.
Front Hum Neurosci. 2017 Feb 3;11:15. doi: 10.3389/fnhum.2017.00015. eCollection 2017.
We present a non-parametric approach to prediction of the n-back ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.
我们提出了一种非参数方法，用于使用近红外光谱（NIRS）数据预测n-back ∈ {1, 2}任务，以此作为心理负荷的替代指标。具体而言，我们专注于通过这些任务诱发的大脑血液动力学反应来测量心理负荷，从而实现其在现实场景中（例如对话难度）进行检测的潜力。我们的方法利用NIRS时间序列成分中固有的内在线性，采用单步回归策略。我们通过数学分析证明了我们方法的正确性。此外，与文献中的现有技术相比，我们在受试者间设置中研究了我们模型的性能，结果表明在这些任务的预测方面有显著改进（女性和男性参与者的预测准确率分别为82.50%和86.40%）。此外，我们的实证分析表明，分类器的性能存在性别差异效应（男性数据表现出更高的非线性），并且两性均存在左侧激活，女性的特异性更高。
