参考文献/References:
[1] VAUGHAN T M,HEETDERKS W J,TREJO L J,et al.Brain-computer interface technology: A review of the second International Meeting[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society,2003,11(2):94-109.DOI:10.1109/tnsre.2003.814799.
[2] V?RBU K,MUHAMMAD N,MUHAMMAD Y.Past, present, and future of EEG-based BCI applications[J].Sensors,2022,22(9):3331.DOI:10.3390/s22093331.
[3] SCHWARTZ A B,CUI X T,WEBER D J,et al.Brain-controlled interfaces: Movement restoration with neural prosthetics[J].Neuron,2006,52(1):205-220.DOI:10.1016/j.neuron.2006.09.019.
[4] KHADEMI Z,EBRAHIMI F,KORDY H M.A review of critical challenges in MI-BCI: From conventional to deep learning methods[J].Journal of Neuroscience Methods,2023,383:109736.DOI:10.1016/j.jneumeth.2022.109736.
[5] ANG K K,CHIN Z Y,WANG Chuanchu,et al.Filter bank common spatial pattern algorithm on BCI competition Ⅳ datasets 2a and 2b[J].Frontiers in Neuroscience,2012,6:39.DOI:10.3389/fnins.2012.00039.
[6] BASHAR S K,HASSAN A R,BHUIYAN M I H.Identification of motor imagery movements from EEG signals using dual tree complex wavelet transform[C]//International Conference on Advances in Computing, Communications and Informatics.[S.l.]:IEEE Press,2015:290-296.DOI:10.1109/ICACCI.2015.7275623.
[7] LAWHERN V J,SOLON A J,WAYTOWICH N R,et al.EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces[J].Journal of Neural Engineering,2018,15(5):056013.DOI:10.1088/1741-2552/aace8c.
[8] LAWRENCE S,GILES C L,TSOI A C,et al.Face recognition: A convolutional neural-network approach[J].IEEE Transactions on Neural Networks,1997,8(1):98-113.DOI:10.1109/72.554195.
[9] INGOLFSSON T M,HERSCHE M,WANG X,et al.EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces[C]//IEEE International Conference on Systems,Man,and Cybernetics.[S.l.]:IEEE Press,2020:2958-2965.DOI:10.1109/SMC42975.2020.9283028.
[10] BAI S,KOLTER J Z,KOLTUN V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL].(2018-03-04)[2023-05-24] .https://arxiv.org/abs/1803.01271.
[11] MANE R,CHEW E,CHUA K,et al.FBCNet: A multi-view convolutional neural network for brain-computer interface[EB/OL].(2021-03-17)[2023-05-24] .https://arxiv.org/abs/2104.01233.
[12] SONG Yonghao,ZHENG Qingqing,LIU Bingchuan,et al.EEG-Conformer: Convolutional transformer for EEG decoding and visualization[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2022,31:710-719.DOI:10.1109/TNSRE.2022.3230250.
[13] 孔祥浩,马琳,薄洪健,等.CNN与CSP相结合的脑电特征提取与识别方法研究[J].信号处理,2018,34(2):164-173.DOI:10.16798/j.issn.1003-0530.2018.02.006.
[14] ZHANG Ruilong,ZONG Qun,DOU Liqian,,et al.A novel hybrid deep learning scheme for four-class motor imagery classification[J].Journal of Neural Engineering,2019,16(6):066004.DOI:10.1088/1741-2552/ab3471.
[15] KHADEMI Z,EBRAHIMI F,KORDY H M.A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals[J].Computers in Biology and Medicine,2022,143:105288.DOI:10.1016/j.compbiomed.2022.105288.
[16] HU Jie,SHEN Li,SUN Gang.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Ccomputer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:7132-7141.DOI:10.1109/CVPR.2018.00745.
[17] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach:Curran Associates Inc.2017:6000-6010.DOI: 10.48550/arXiv.1706.03762.
[18] CHEN Xia,TENG Xiangbin,CHEN Han,et al.Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX[EB/OL].(2022-07-15)[2023-05-24] .https://arxiv.org/abs/2207.12369.
[19] BRUNNER C,LEEB R,MüLLER-PUTZ G,et al.BCI Competition 2008-Graz data set A[J].Institute for Knowledge Discovery(Laboratory of Brain-Computer Interfaces), Graz University of Technology,2008,16:1-6.
[20] MUSALLAM Y K,ALFASSAM N I,MUHAMMAD G,et al.Electroencephalography-based motor imagery classification using temporal convolutional network fusion[J].Biomedical Signal Processing and Control,2021,69:102826.DOI:10.1016/j.bspc.2021.102826.
[21] SALAMI A,ANDREU-PEREZ J,GILLMEISTER H.EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification[J].IEEE Access,2022,10:36672-36685.DOI:10.1109/ACCESS.2022.3161489.
[22] YANG Lie,SONG Yonghao,MA Ke,et al.Motor imagery EEG decoding method based on a discriminative feature learning strategy[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2021,29:368-379.DOI:10.1109/TNSRE.2021.3051958.
[23] AMIN S U,ALTAHERI H,MUHAMMAD G,et al.Attention-inception and long-short-term memory-based electroencephalography classification for motor imagery tasks in rehabilitation[J].IEEE Transactions on Industrial Informatics,2021,18(8):5412-5421.DOI:10.1109/TII.2021.3132340.
[24] ALTAHERI H,MUHAMMAD G,ALSULAIMAN M,et al.Deep learning techniques for classification of electroencephalogram(EEG)motor imagery(MI)signals: A review[J].Neural Computing and Applications,2021,35(1):1-42.DOI:10.1007/s00521-021-06352-5.
[25] KIM S J,LEE D H,LEE S W.Rethinking CNN architecture for enhancing decoding performance of motor imagery-based EEG signals[J].IEEE Access,2022,10:96984-96996.DOI:10.1109/ACCESS.2022.3204758.