IEEE Trans Neural Syst Rehabil Eng. 2019 May;27(5):867-875. doi: 10.1109/TNSRE.2019.2906371. Epub 2019 Mar 20.
Recent research has unearthed that blink rate variability (BRV) can be employed as a psychophysiological measure. However, its efficiency for mental state recognition (MSR) has not been investigated yet. Because BRV can indicate dynamics inherent in eye blinks, we conjectured that BRV might exhibit stronger abilities for the MSR if compared with blink rate (BR), known as the leading indicator derived from eye blinks for MSR. Therefore, in this paper, we attempted to differentiate between high and low cognitive loads of an individual through the analyses of BR and BRV, respectively, which could be viewed as a preliminary study for comparing their MSR abilities. First, an n -back experiment was performed to collect data. Then, in order to characterize the phenomenon of BRV, the features were extracted from its time and frequency domains, respectively. Finally, the area under the curve (AUC) values of BRV and BR for MSR were estimated by the ten commonly used classifiers, respectively. The results indicated that BRV achieves significantly higher AUC values than BR, which suggests its strong potentiality for MSR. In sum, the BRV may prove to be a promising method for the MSR, which should be considered in the future.
最近的研究发现眨眼率变异性（BRV）可用作心理生理指标。然而，其在心理状态识别（MSR）中的效率尚未得到研究。由于 BRV 可以指示眨眼固有的动态，我们推测，如果与作为 MSR 中源自眨眼的主要指标的眨眼率（BR）相比，BRV 可能会表现出更强的 MSR 能力。因此，在本文中，我们尝试通过分别分析 BR 和 BRV 来区分个体的高和低认知负荷，这可以看作是比较它们的 MSR 能力的初步研究。首先，进行 n 回实验以收集数据。然后，为了表征 BRV 的现象，分别从其时间和频率域中提取特征。最后，通过十种常用的分类器分别估计 BRV 和 BR 的 MSR 的曲线下面积（AUC）值。结果表明，BRV 的 AUC 值明显高于 BR，这表明其在 MSR 中具有很强的潜力。总之，BRV 可能被证明是 MSR 的一种很有前途的方法，在未来应该加以考虑。
