School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China.
Sensors (Basel). 2020 Sep 8;20(18):5122. doi: 10.3390/s20185122.
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.
认知负荷与工作记忆的匹配是有效学习的关键，学习过程中的认知努力会在各种生理参数中产生神经反应。因此，通过使用生理测量来探索自动认知负荷模式识别具有重要意义。首先，这项工作提取了 33 个常用的生理特征来量化自主和中枢神经系统活动。其次，我们通过顺序后向选择和粒子群优化算法选择了用于认知负荷识别的关键特征子集。最后，通过比较几种分类器的性能构建了认知负荷条件的模式识别模型。我们将开放数据集的样本分组，形成两个二进制分类问题：（1）认知负荷状态与基线状态；（2）认知负荷不匹配状态与认知负荷匹配状态。决策树分类器对认知负荷与基线的分类达到了 96.3%的准确率，支持向量机对认知负荷不匹配与认知负荷匹配的分类达到了 97.2%的准确率。认知负荷和基线状态在主动思维的活跃程度和自主神经系统的三个活动特征上是可区分的。认知负荷不匹配和匹配状态在主动思维的活跃程度和自主神经系统的两个活动特征上是可区分的。
