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 k-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.

中文翻译：

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数学课堂中学生兴趣分类的混合EMD-小波EEG特征提取方法

情境兴趣（SI）是可以改善学生的学习并增加所学知识的有前途的状态之一。基于脑电图（EEG-）的SI检测可以帮助理解SI神经科学的原因，从而可以解释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。进行了SI和个人兴趣（PI）问卷以及知识测试，以衡量学生的兴趣和知识水平。提出了一种结合经验模态分解（EMD）和小波变换的混合方法，并将其用于特征提取。k近邻（wkNN）。较高的分类准确度（85.7％），敏感性（81.8％）和特异性（90％）表明，高SI学生的脑震荡模式与低SI学生或没有SI的学生有所不同。另外，该结果表明，Δ节律可能对认知加工有显着影响。