Automated visual stimuli evoked multi-channel electroencephalograph (EEG) signals classification is in a nascent stage but is receiving progressive attention from researchers. The conventional techniques existing for EEG classification tasks overlook the spatial attributes of EEG signals, which contain spatial data information to characterize an image from visual stimuli evoked EEG signal. In this paper, a Deep learning structure implemented on STFT (Short Term Fourier Transform) generated spectrogram images and a Capsule Network (EEGCapsNet) is proposed. In this architecture, the time and frequency domain as well as, spatial attributes of the multi-channel EEG signals are extracted to build the spectrogram image and are fed to the proposed EEGCapsNet for classifying those EEG signals which are acquired from the stimuli evoked visual experience while seeing an image. For this purpose, two different EEG datasets (namely Perceive and MindBig) are trained on the proposed network. The highest average accuracy of 81.59% and 84.62% is reported for the proposed EEGCapsNet.

自动视觉刺激诱发的多通道脑电图 (EEG) 信号分类处于初期阶段，但正受到研究人员的逐渐关注。现有的用于 EEG 分类任务的传统技术忽略了 EEG 信号的空间属性，其中包含空间数据信息，用于从视觉刺激诱发的 EEG 信号中表征图像。在本文中，提出了在 STFT（短期傅立叶变换）生成的频谱图图像和胶囊网络 (EEGCapsNet) 上实现的深度学习结构。在这种架构中，时域和频域以及，提取多通道 EEG 信号的空间属性以构建频谱图图像，并将其馈送到所提出的 EEGCapsNet，用于对从观看图像时的刺激诱发的视觉体验中获取的 EEG 信号进行分类。为此，在提议的网络上训练了两个不同的 EEG 数据集（即 Perceive 和 MindBig）。建议的 EEGCapsNet 报告的最高平均准确率为 81.59% 和 84.62%。