GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Zhijiang Wan; Wangxinjun Cheng; Manyu Li; Renping Zhu; Wenfeng Duan

doi:10.3389/fnins.2023.1160040

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition

Front Neurosci. 2023 Apr 13:17:1160040. doi: 10.3389/fnins.2023.1160040. eCollection 2023.

Authors

Zhijiang Wan^{1

2

3}, Wangxinjun Cheng⁴, Manyu Li², Renping Zhu^{2

3

5}, Wenfeng Duan¹

Affiliations

¹ The First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, Jiangxi, China.
² School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China.
³ Industrial Institute of Artificial Intelligence, Nanchang University, Nanchang, Jiangxi, China.
⁴ Queen Mary College of Nanchang University, Nanchang University, Nanchang, Jiangxi, China.
⁵ School of Information Management, Wuhan University, Wuhan, China.

Abstract

Background: Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition.

Method: Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model.

Results: Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%.

Conclusion: Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.

Keywords: EEG attention; EEG signal; SSVEPs; group depth-wise convolution; stimulation frequency recognition.