Categorizing objects from MEG signals using EEGNet

Abstract

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.

Keywords: Deep learning; Feature fusion; Magnetoencephalography; Neural decoding.

Fig. 1

Durations of different ERP components (gray blocks represent the durations of P1, N1, P2a, and P2b) (Qin et al. 2016)

Fig. 2

Layout of sensor chips provided in Elekta neuroimage TRUIX user’s manual. Each number in the figure corresponds to a group of sensors including 1 magnetometer and 2 gradiometers

Fig. 3

The proposed spatiotemporal feature fusion architecture

Fig. 4

Binary and four-class classification results at the individual level and across subject level

Fig. 5

Classification accuracies of different time intervals from 0 to 600 ms after stimulus onset