计算机科学
卷积神经网络
脑-机接口
神经反射
卷积(计算机科学)
人工智能
模式识别(心理学)
脑电图
代表(政治)
级联
人工神经网络
机器学习
心理学
神经科学
政治
化学
法学
色谱法
政治学
作者
Yangsong Zhang,Huan Cai,Lei Nie,Peng Xu,Sirui Zhao,Cuntai Guan
标识
DOI:10.1016/j.neunet.2021.08.019
摘要
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.
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