文献类型：期刊文献

英文题名：Squeeze and Excitation-Based Multiscale CNN for Classification of Steady-State Visual Evoked Potentials

作者：Jin, Jing[1,2];Wu, Xiao[3];Daly, Ian[4];Chen, Weijie[3];He, Xinjie[3];Wang, Xingyu[3];Cichocki, Andrzej[5,6]

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

年份：2025

卷号：12

期号：5

起止页码：5822

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20244617355476);WOS:【SCI-EXPANDED(收录号:WOS:001433294700029)】；

基金：This work wassupported in part by STI 2030-major Projects under Grant 2022ZD0208900; in part by the Grant National Natural Science Foundation of China under Grant 62176090; in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX; and in part by Project of Jiangsu Province Science and Technology Plan Special Fund in 2022 (Key Research and Development Plan Industry Foresight, Fundamental Research Fund for the Central Universities JKH01231636 and Key Core Technologies) under Grant BE2022064-1.

语种：英文

外文关键词：Feature extraction; Electroencephalography; Internet of Things; Visualization; Accuracy; Target recognition; Frequency modulation; Convolutional neural networks; Decoding; Convolution; Brain-computer interface (BCI); convolutional neural network (CNN); multiscale fusion; squeeze and excitation module (SEM); steady-state visual evoked potentials (SSVEPs)

摘要：Brain-computer interface (BCI) technology enables the control of external devices by recognizing user intentions. Steady-state visual evoked potential (SSVEP)-based BCI technology has been widely applied in the field of Internet of Things (IoT) device control, including smart healthcare, smart homes, and robotics, and has achieved significant results. However, as the field of BCI-based IoT device control is still in its development stage, there remains considerable room for improvement in terms of accuracy, efficiency, and cost. Therefore, enhancing the classification accuracy of SSVEP decoding using a short time window, reducing both human and material costs, and improving work efficiency are crucial for the theoretical research and engineering applications of BCI technology in IoT device control. Based on this, we propose a novel approach to address the challenge of high-accuracy feature extraction within brief timeframes. Our approach integrates a multiscale convolutional neural network with a squeeze excitation module (SEMSCNN). This fusion leverages convolutional neural networks (CNNs)' local feature learning capacity and the advantageous feature importance distinction offered by the squeeze excitation mechanism. First, the electroencephalogram signals are band-pass filtered into distinct frequency bands and frequency band and channel features are extracted by a two-layer convolution. Then, temporal features are extracted via a multibranch convolution of different scales. Finally, the squeeze and excitation (SE) module is introduced to learn the interdependence between features to improve the quality of the extracted features. The first stage of training exploits statistical commonalities across research participants by learning the global model, and the second stage fine-tunes each participant's features separately by exploiting participant-specific differences in features. We evaluate our SEMSCNN model on two large public datasets, Benchmark and BETA, and we compare our model to other state-of-the-art models in order to evaluate the effectiveness of our proposed network. Our experimental results indicate that our method effectively improves the accuracy of target recognition and information transfer rate under short-duration stimuli, showing a significant advantage compared to other baseline methods. This provides a broad prospect for the practical application of BCIs in the field of IoT.