School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China; Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China.
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Neural Netw. 2021 Dec;144:129-137. doi: 10.1016/j.neunet.2021.08.019. Epub 2021 Aug 20.
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.
注意心理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，检测方法的性能仍不尽如人意。其中一个挑战是为脑电图（EEG）数据找到一个合适的表示方法，该方法可以保留时间信息并保持空间拓扑特征。受脑机接口（BCI）领域深度学习（DL）方法的启发，将 EEG 信号的 3D 表示引入到注意检测任务中，并提出了一种具有级联和并行卷积操作的 3D 卷积神经网络模型。该模型利用三个级联块，每个块由两个并行的 3D 卷积分支组成，同时提取多尺度特征。在一个包含 26 个被试的公共数据集上进行评估，与基线方法相比，该模型在被试内、被试间和被试自适应分类场景下均表现出更好的性能。本研究证明了 3D CNN 模型在检测注意心理状态方面具有很大的潜力。
