Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka-Shi, Fukuoka 812-8582, Japan.
Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka-Shi, Fukuoka 819-0395, Japan.
Sensors (Basel). 2018 Oct 30;18(11):3691. doi: 10.3390/s18113691.
Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.
参与度被描述为个体参与某项活动时能够忽略其他影响的状态。参与度水平对于获得良好的表现非常重要，尤其是在学习条件下。已经提出了许多使用脑电图（EEG）、心电图（ECG）和近红外光谱（NIRS）来识别参与度的方法。然而，结果要么不尽如人意，要么需要许多通道。在本研究中，我们引入了一种用于参与度识别的低密度混合系统的实现。我们使用了两个电极的无线 EEG、无线 ECG 和两个无线 NIRS 通道来测量认知任务中的参与度识别。我们使用眼电图（EOG）和眼动追踪记录眼球运动以进行数据标记。我们使用基于相关性的特征选择和 k-最近邻算法的组合来计算识别准确率。之后，我们对该混合系统与独立系统进行了比较研究。结果表明，混合系统具有可接受的实际应用准确率（71.65±0.16%）。相比之下，纯 EEG 系统的准确率为（65.73±0.17%），纯 ECG 系统为（67.44±0.19%），纯 NIRS 系统为（66.83±0.17%）。总体而言，我们的结果表明，所提出的方法可用于提高参与度识别的性能。
