FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection

• Published in: Journal of neuroscience methods Vol. 379; p. 109674
• Main Authors: Yao, Huiming, Liu, Ke, Deng, Xin, Tang, Xianlun, Yu, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2022
• Subjects: Brain-computer interface; Feature fusion; Learning across multi-stimulus; SSVEP; Brain-computer interface; SSVEP; Learning across multi-stimulus; Feature fusion
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2022.109674

Steady-state visual evoked potential (SSVEP) is a prevalent paradigm of brain-computer interface (BCI). Recently, deep neural networks (DNNs) have been employed for SSVEP target recognition. However, current DNN models can not fully extract information from SSVEP harmonic components, and ignore the influence of non-target stimuli. To employ information of multiple sub-bands and non-target stimulus data, we propose a DNN model for SSVEP target detection, i.e., FB-EEGNet, which fuses features of multiple neural networks. Additionally, we design a multi-label for each sample and optimize the parameters of FB-EEGNet across multi-stimulus to incorporate the information from non-target stimuli. Under the subject-specific condition, FB-EEGNet achieves the average classification accuracies (information transfer rate (ITR)) of 76.75 % (50.70 bits/min) and 89.14 % (70.45 bits/min) in a time widow of 0.7 s under the public 12-target dataset and our experimental 9-target dataset, respectively. Under the cross-subject condition, FB-EEGNet achieved mean accuracies (ITRs) of 81.72 % (67.99 bits/min) and 92.15 % (76.12 bits/min) on the public and experimental datasets in a time window of 1 s, respectively. FB-EEGNet shows superior performance than CCNN, EEGNet, CCA and FBCCA both for subject-dependent and subject-independent SSVEP target recognition. FB-EEGNet can effectively extract information from multiple sub-bands and cross-stimulus targets, providing a promising way for extracting deep features in SSVEP using neural networks. •Fusing features from multiple sub-bands in deep neural network improves the SSVEP recognition performance.•Multi-label encoding can handle the influence of non-target stimuli, and enhance SSVEP frequency detection performance.•Two-stage DNN training strategy is employed to extract non-linear information under a small dataset.