The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain–computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.

注意精神状态的检测在神经反馈过程和注意力缺陷多动障碍（ADHD）的治疗中起着至关重要的作用。然而，检测方法的性能仍然不能令人满意。挑战之一是为脑电图 (EEG) 数据找到合适的表示，它可以保留时间信息并保持空间拓扑特征。受脑机接口（BCI）领域研究中的深度学习（DL）方法的启发，将脑电信号的 3D 表示引入注意力检测任务，并提出了具有级联和并行卷积操作的 3D 卷积神经网络模型. 该模型使用了三个级联块，每个块由两个平行的 3D 卷积分支组成，同时提取多尺度特征。在包含 26 个主题的公共数据集上进行评估，与主题内、主题间和主题自适应分类场景下的基线方法相比，所提出的模型取得了更好的性能。这项研究证明了 3D CNN 模型在检测注意力集中状态方面的潜力。