文献类型：期刊文献

英文题名：Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention

作者：Wu, Xiao[1];Daly, Ian[2];Lau, Andrew Ty[3];Chen, Weijie[1];Wang, Chongfeng[1];Cichocki, Andrzej[4,5];Jin, Jing[6,7]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Brain Comp Interfacing & Neural Engn Lab, Colchester CO4 3SQ, Essex, England;[3]Shanghai Lansheng Brain Hosp Investment Co Ltd, Shanghai 200336, Peoples R China;[4]Syst Res Inst Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland;[5]Nicolaus Copernicus Univ UMK, PL-87100 Torun, Poland;[6]East China Univ Sci & Technol, Sch Math, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[7]ECUST Med Engn Integrat Innovat Ctr, Shanghai 200237, Peoples R China

年份：2026

卷号：35

起止页码：4339

外文期刊名：IEEE TRANSACTIONS ON IMAGE PROCESSING

收录：;EI(收录号：20261720579185);WOS:【SCI-EXPANDED(收录号:WOS:001754884400002)】；

基金：This work was supported in part by the Brain Science and Brain-Like Intelligence Technology-National Science and Technology Major Project under Grant 2022ZD0208900; in part by the National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; in part by the Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 under Grant BE2022064-1; in part by the Key Research and Development Plan Industry Foresight, Fundamental Research Fund for the Central Universities and Key Core Technologies under Grant JKH01241605; and in part by Lingang Laboratory under Grant LGL8998.

语种：英文

外文关键词：Filtering; Filters; Spatial filters; Band-pass filters; Filter banks; Circuits and systems; Active filters; Oscillators; Videos; Electronic mail; Brain-computer interface; steady-state visual evoked potential; deep neural network; pyramid squeeze attention; target recognition

摘要：Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high information transfer rates. However, how to fully utilize the potential information of existing subjects to realize the mining of common information among different subjects and then realize the information migration in a small amount of data scenarios is a difficult problem faced by current research. In order to solve the above problems, this study proposes a deep neural network based on the pyramid squeeze attention (PSA-DNN) mechanism to enhance the performance of SSVEP-BCI through common information migration. Specifically, the band-pass filtered EEG signals were first Fourier transformed to obtain the frequency domain information; subsequently, the frequency domain information is input into a deep neural network, followed by a spatial convolution step to extract spatial domain information. In order to further enhance the quality of information extraction, a pyramid attention module is introduced into the network to realize the enhancement of frequency domain and spatial domain information. Time domain information from the EEG signals is then mined using temporal convolution. Finally, the full connectivity layer is used to output the recognition results. The model is trained in a three-stage stepped approach for SSVEP target recognition. The first stage uses data from all participants in the training set for common information learning and transfers the model parameters trained in the first stage to the network model in the second stage. In the second stage, some of the information from participants in the test set is used for fine-tuning and to mine personalized information from these new participants. The third stage uses the remaining data from participants in the test set to produce classification results. The proposed method is systematically evaluated using the Benchmark and BETA datasets, where it demonstrates favorable performance compared to established baselines. These findings contribute theoretical insights and methodological References for the application of SSVEP-based brain-computer interfaces in real-world scenarios.