Student attention is an indispensable input for uncovering their goals, intentions, and interests, which prove to be invaluable for a multitude of research areas, ranging from psychology to interactive systems. However, most existing methods to classify attention fail to model its complex nature. To bridge this gap, we propose AttentioNet, a novel Convolutional Neural Network-based approach that utilizes Electroencephalography (EEG) data to classify attention into five states: Selective, Sustained, Divided, Alternating, and relaxed state. We collected a dataset of 20 subjects through standard neuropsychological tasks to elicit different attentional states. The average across-student accuracy of our proposed model at this configuration is 92.3% (SD=3.04), which is well-suited for end-user applications. Our transfer learning-based approach for personalizing the model to individual subjects effectively addresses the issue of individual variability in EEG signals, resulting in improved performance and adaptability of the model for real-world applications. This represents a significant advancement in the field of EEG-based classification. Experimental results demonstrate that AttentioNet outperforms a popular EEGnet baseline (p-value < 0.05) in both subject-independent and subject-dependent settings, confirming the effectiveness of our proposed approach despite the limitations of our dataset. These results highlight the promising potential of AttentioNet for attention classification using EEG data.

翻译：学生注意力是揭示其目标、意图和兴趣不可或缺的输入信息，对从心理学到交互系统等多个研究领域具有重要价值。然而，现有的大多数注意力分类方法未能捕捉其复杂特性。为弥补这一不足，我们提出AttentioNet——一种基于卷积神经网络的创新方法，利用脑电图数据将注意力划分为五种状态：选择性注意力、持续性注意力、分配性注意力、交替性注意力和放松状态。我们通过标准神经心理学任务收集了20名受试者的数据集，以诱发不同注意力状态。在该配置下，所提模型的学生间平均准确率为92.3%（标准差=3.04），非常适用于终端用户应用。我们基于迁移学习的个性化方法有效解决了脑电信号个体差异问题，提升了模型在真实场景中的性能和适应性，这标志着脑电分类领域的重大进展。实验结果表明，在受试者独立和受试者依赖两种设置下，AttentioNet均优于流行的EEGnet基线（p值<0.05），尽管数据集存在局限性，仍证实了所提方法的有效性。这些结果凸显了AttentioNet在利用脑电数据进行注意力分类方面的巨大潜力。