Department of Electronics Engineering, Future University, Khartoum, Sudan.
Centre of Intelligent Signal and Imaging Research & Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia.
Comput Intell Neurosci. 2021 Jan 23;2021:6617462. doi: 10.1155/2021/6617462. eCollection 2021.
Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted -nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.
情境兴趣（SI）是一种很有前途的状态，可以提高学生的学习效果并增加所学知识。基于脑电图（EEG）的 SI 检测可以帮助我们理解 SI 的神经科学原因，从而解释 SI 在学生学习中的作用。在这项研究中，根据问卷选择了 26 名参与者参加数学课堂实验。SI 和个人兴趣（PI）问卷以及知识测试用于衡量学生的兴趣和知识水平。开发并采用了一种结合经验模态分解（EMD）和小波变换的混合方法进行特征提取。多变量方差分析（MANOVA）测试表明，该方法具有显著差异，加权最近邻（wkNN）的分类性能也优于其他方法。高分类准确率为 85.7%，灵敏度为 81.8%，特异性为 90%，这表明高 SI 学生的脑振荡模式与低 SI 或无 SI 学生的脑振荡模式有些不同。此外，研究结果表明，δ 节律可能对认知处理有重要影响。
