Page 218 - 《软件学报》2021年第12期
P. 218

3882                                Journal of Software  软件学报 Vol.32, No.12, December 2021

          [2]    Li J, Liu GZ, Gao J. Emotion classification based on EEG signal. Journal of Beijing Information Science &Technology University,
             2017,32(2):34−39 (in Chinese with English abstract).
          [3]    Sun  W, Huang J, Li NL,  et  al.  BCI  assisted dynamic target selection technique.  Ruan Jian  Xue Bao/Journal of Software,
             2018,29(Suppl.(2)):108−119 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18022.htm
          [4]    Zhang JR, Wang  G.  Research on  emotion  recognition based on  EEG signals.  Application  Research of  Computers, 2019,36(11):
             3306−3309 (in Chinese with English abstract).
          [5]    Jiang JF, Zeng Y, Lin ZM, et al. Review on EEG.Based Emotion Assessment. Joumal of Information Engineering University,
             2016,17(6):686−693 (in Chinese with English abstract).
          [6]    Krizhevsky A, Sutskever A, Hinton I, et al. ImageNet classification with deep convolutional neural networks. In: Proc. of the Int’l
             Conf. on Neural Information Processing Systems. 2012. 1097−1105.
          [7]    Graves A. Generating sequences with recurrent neural networks. arXiv preprint arXiv: 1308.0850, 2013.
          [8]    Karpathy A, Toderici G, Shetty S, et al. Large-scale video classification with convolutional neural networks. In: Proc. of the IEEE
             Conf. on Computer Vision and Pattern Recognition. IEEE, 2014. 1725−1732.
          [9]    Lecun Y, Bottou L, Bengio Y, et al.  Gradient-based learning  applied to document recognition. Proc. of  the IEEE, 1998,86(11):
             2278−2324.
         [10]    Qian Z, Li PF, Zhou GD, et al. Speculation and negation scope detection via bidirectional lstm neural networks. Ruan Jian Xue
             Bao/Journal of Software, 2018,29(8):2427−2447  (in Chinese with English abstract).  http://www.jos.org.cn/1000-9825/5485.htm
             [doi: 10.13328/j.cnki.jos.005485]
         [11]    Ng JYH, Hausknecht M, Vijayanarasimhan S, et al. Beyond short snippets: Deep networks for video classification. In: Proc. of the
             IEEE Conf. on Computer Vision and Pattern Recognition. IEEE, 2015. 4694−4702.
         [12]    Bashivan P, Rish I, Yeasin M, et al. Learning representations from EEG with deep recurrent-convolutional neural networks. In:
             Proc. of the Int’l Conf. on Learning Representations. 2015. http://arxiv.org/abs/1511.06448
         [13]    Hefron RG, Borghetti BJ, Christensen JC, et al. Deep long short-term memory structures model temporal dependencies improving
             cognitive workload estimation. Pattern Recognition Letters, 2017,94(C):96−104.
         [14]    Zhang D, Yao L, Zhang X, et al. EEG-based intention recognition from spatio-temporal representations via cascade and parallel
             convolutional recurrent neural networks. arXiv preprint arXiv: 1708.06578, 2017.
         [15]    Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: A compact convolutional network for EEG-based brain-computer interfaces.
             Journal of Neural Engineering, 2018,15(5):056013.
         [16]    Alhagry S, Aly A, Reda A. Emotion recognition based on EEG using LSTM recurrent neural network. Int’l Journal of Advanced
             Computer Science & Applications, 2017,8(10):355−358.
         [17]    Soleymani M, Asghari-Esfeden S, Fu Y, et al. Analysis of EEG signals and facial expressions for continuous emotion detection.
             IEEE Trans. on Affective Computing, 2016,7(1):17−28.
         [18]    Salama ES, El-Khoribi RA,  Shoman ME,  et  al. EEG-based emotion recognition  using  3D convolutional  neural  networks. Int’l
             Journal of Advanced Computer Science and Applications, 2018,9(8):329−337.
         [19]    Chen  JX, Zhang  PW, Mao ZJ,  et  al. Accurate EEG-based emotion recognition  on combined  features  using  deep convolutional
             neural networks. IEEE Access, 2019,7:44317−44328.
         [20]    Chen JX, Jiang DM, Zhang YN.  A  hierarchical bidirectional  GRU  model with  attention for EEG-based  emotion  classification.
             IEEE Access, 2019,7:118530−118540. [doi: 10.1109/ACCESS.2019.2936817]
         [21]    Koelstra S, Muhl C,  Soleymani M,  et  al.  Deap:  A database for  emotion  analysis; using physiological signals. IEEE  Trans. on
             Affective Computing, 2011,3(1):18−31.
         [22]    Balconi M, Mazza G. Brain  oscillations and BIS/BAS  (behavioral inhibition/activation  system) effects  on  processing masked
             emotional cues. ERS/ERD and coherence measures of alpha band. Int’l Journal of Psychophysiology, 2009,74(2):158−165.
         附中文参考文献:
          [1]  卢官明,袁亮,杨文娟,等.基于长短期记忆和卷积神经网络的语音情感识别.南京邮电大学学报(自然科学版),2018,38(5):63−69.
          [2]  李娟,刘国忠,高洁.基于脑电信号的情绪分类.北京信息科技大学学报(自然科学版),2017,32(2):34−39.
   213   214   215   216   217   218   219   220   221   222   223