Magnetoencephalography (MEG) signals have demonstrated their practical application to reading human minds. Current neural decoding studies have made great progress to build subject-wise decoding models to extract and discriminate the temporal/spatial features in neural signals. In this paper, we used a compact convolutional neural network—EEGNet—to build a common decoder across subjects, which deciphered the categories of objects (faces, tools, animals, and scenes) from MEG data. This study investigated the influence of the spatiotemporal structure of MEG on EEGNet’s classification performance. Furthermore, the EEGNet replaced its convolution layers with two sets of parallel convolution structures to extract the spatial and temporal features simultaneously. Our results showed that the organization of MEG data fed into the EEGNet has an effect on EEGNet classification accuracy, and the parallel convolution structures in EEGNet are beneficial to extracting and fusing spatial and temporal MEG features. The classification accuracy demonstrated that the EEGNet succeeds in building the common decoder model across subjects, and outperforms several state-of-the-art feature fusing methods.

脑磁图 (MEG) 信号已经证明了它们在阅读人类思想方面的实际应用。当前的神经解码研究在构建主题解码模型以提取和区分神经信号中的时间/空间特征方面取得了很大进展。在这篇论文中，我们使用了一个紧凑的卷积神经网络——EEGNet——构建了一个跨主题的通用解码器，它从 MEG 数据中破译了物体的类别（面部、工具、动物和场景）。本研究调查了 MEG 的时空结构对 EEGNet 分类性能的影响。此外，EEGNet 将其卷积层替换为两组并行卷积结构，以同时提取空间和时间特征。我们的结果表明，输入 EEGNet 的 MEG 数据的组织对 EEGNet 的分类精度有影响，EEGNet 中的并行卷积结构有利于提取和融合时空 MEG 特征。分类准确性表明 EEGNet 成功地构建了跨主题的通用解码器模型，并且优于几种最先进的特征融合方法。