[1] |
WIDGE A S , DOUGHERTY D D , MORITZ C T . Affective braincomputer interfaces as enabling technology for responsive psychiatric stimulation[J]. Brain-Computer Interfaces, 2014,1(2): 126-136.
|
[2] |
ALGHOWINEM S , GOECKE R , WAGNER M ,et al. Multimodal depression detection:fusion analysis of paralinguistic,head pose and eye gaze behaviors[J]. IEEE Transactions on Affective Computing, 2018,9(4): 478-490.
|
[3] |
CAPONETTI L , BUSCICCHIO C A , CASTELLANO G . Biologically inspired emotion recognition from speech[J]. EURASIP Journal on Advances in Signal Processing, 2011(1): 1-10.
|
[4] |
ARRIAGA O , VALDENEGRO-TORO M , PLOGER P . Real-time convolutional neural networks for emotion and gender classification[J]. arXiv preprint, 2017,arXiv:1710.07557.
|
[5] |
LAN Z R , SOURINA O , WANG L P ,et al. Domain adaptation techniques for EEG-based emotion recognition:a comparative study on two public datasets[J]. IEEE Transactions on Cognitive and Developmental Systems, 2019,11(1): 85-94.
|
[6] |
娄晓光, 陈兰岚, 宋振振 . 基于多源域迁移学习的脑电情感识别[J]. 计算机工程与设计, 2020,41(7): 2011-2018.
|
|
LOU X G , CHEN L L , SONG Z Z . EEG emotion recognition based on multi-source domain transfer learning[J]. Computer Engineering and Design, 2020,41(7): 2011-2018.
|
[7] |
PAN S J , YANG Q . A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359.
|
[8] |
GANIN Y , LEMPITSKY V . Unsupervised domain adaptation by backpropagation[C]// Proceedings of the 32nd International Conference on Machine Learning.[S.l.:s.n.], 2015: 325-333.
|
[9] |
GUI L , XU R F , LU Q ,et al. Negative transfer detection in transductive transfer learning[J]. International Journal of Machine Learning &Cybernetics, 2018,9(2): 185-197.
|
[10] |
ZHENG W L , LYU B L . Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Transactions on Autonomous Mental Development, 2015,7(3): 162-175.
|
[11] |
DUAN R N , ZHU J Y , LYU B L . Differential entropy feature for EEG-based emotion classification[C]// Proceedings of the 6th International IEEE/ EMBS Conference on Neural Engineering. Piscataway:IEEE Press, 2013.
|
[12] |
BAI S J , KOLTER J Z , KOLTUN V ,et al. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J]. arXiv preprint, 2018,arXiv:1803.01271.
|
[13] |
WILSON G , DOPPA J R , COOK D . Multi-source deep domain adaptation with weak supervision for time-series sensor data[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data. New York:ACM Press, 2020: 6-19.
|
[14] |
FAWAZ H I , FORESTIER G , WEBER J ,et al. Deep learning for time series classification:a review[J]. Data Mining and Knowledge Discovery, 2019,33(4): 917-963.
|
[15] |
XIE Q Z , DAI Z H , DU Y L ,et al. Controllbale invariance through adversarial feature learning[J]. arXiv preprint, 2017,arXiv:1705.11122.
|
[16] |
GANIN Y , USTINOVA E , AJAKAN H ,et al. Domain-adversarial training of neural networks[J]. Journal of Machine Learning Research, 2017,17(1): 2096-2030.
|
[17] |
WANG Z G , YAN W Z , OATES T . Time series classification from scratch with deep neural networks:a strong baseline[C]// Proceedings of the 2017 International Joint Conference on Neural Networks. Piscataway:IEEE Press, 2017.
|
[18] |
FAWAZ H I , FORESTIER G , WEBER J ,et al. Transfer learning for time series classification[C]// Proceedings of the 2018 IEEE International Conference on Big Data. Piscataway:IEEE Press, 2018.
|
[19] |
LI H , JIN Y M , ZHENG W L ,et al. Cross-subject emotion recognition using deep adaptation networks[C]// Proceedings of the 25th International Conference on Neural Information Processing. Cham:Springer, 2018: 403-413.
|
[20] |
PAN S J , TSANG I W , KWOK J T ,et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011,22(2): 199-210.
|