{"type":"ScholarlyArticle","title":"ATTA: Adaptive Test-Time Adaptation for Multi-Modal Sleep Stage Classification","authors":[{"name":"Ziyu Jia","slug":"ziyu-jia","given":"Ziyu","family":"Jia","affiliation":"Brainnetome Center , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Xihao Yang","slug":"xihao-yang","given":"Xihao","family":"Yang","affiliation":"Brainnetome Center , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Chenyang Zhou","slug":"chenyang-zhou","given":"Chenyang","family":"Zhou","affiliation":"Brainnetome Center , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Haoyang Deng","slug":"haoyang-deng","given":"Haoyang","family":"Deng","affiliation":"Brainnetome Center , Institute of Automation , Chinese Academy of Sciences , Beijing , China"},{"name":"Tianzi Jiang","slug":"tianzi-jiang","given":"Tianzi","family":"Jiang","affiliation":"Brainnetome Center , Institute of Automation , Chinese Academy of Sciences , Beijing , China"}],"abstract":"Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. Although some recent studies have made great strides in sleep stage classification performance, direct application to multi-modal sleep data with cross-domain distributional variations still poses challenges: 1) How to retain the sleep knowledge acquired by the model from the source domain during cross-domain adaptation to avoid catastrophic forgetting. 2) How to evaluate the contribution of different modalities in identifying specific sleep stages to serve test-time adaptation (TTA). 3) How to dynamically adapt the sleep model to different distribution shift in data domains of different subjects. To address these challenges, we propose an Adaptive Test-Time Adaptation (ATTA) method, a multi-modal test-time adaptation method for sleep stage classification. Specifically, the intra-modal retained-adaptive module is proposed for adapting to the target domain data while retaining the sleep knowledge acquired from the source domain to avoid catastrophic forgetting. The inter-modal contribution assessment module is designed to adaptively assess the contribution of each modality to the identification of specific sleep stages. Furthermore, the adaptive learning rate strategy utilizes a memory bank to record data from different subjects during testing, and based on this, it measures the differences between the target subject and those in the memory bank. According to the difference, the model adapts to the subject samples with different learning rates. We conduct experiments on mutual migration on two sleep datasets, SleepEDF and SHHS. The results show that our ATTA method outperforms state-of-the-art baselines in sleep stage classification.","doi":"10.24963/ijcai.2024/650","url":"https://ijcai.org/proceedings/2024/650","pdf":"https://ijcai.org/proceedings/2024/0650.pdf","markdown":"https://ijcai.org/proceedings/2024/650.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/650","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":650,"pages":{"first":5882,"last":5890},"keywords":["Multidisciplinary Topics and Applications: MTA: Health and medicine","Machine Learning: ML: Applications","Machine Learning: ML: Time series and data streams","Humans and AI: HAI: Brain sciences"],"videos":[],"references":[{"position":1,"raw":"[Berry et al., 2012] Richard B Berry, Rohit Budhiraja, Daniel J Gottlieb, David Gozal, Conrad Iber, Vishesh K Kapur, Carole L Marcus, Reena Mehra, Sairam Parthasarathy, Stuart F Quan, et al. Rules for scoring res- piratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and associated events. Journal of clinical sleep medicine, 8(05):597-619, 2012.","title":"Rules for scoring respiratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and associated events","authors":"Rohit Richard B Berry, Budhiraja, J Daniel, David Gottlieb, Conrad Gozal, Iber, K Vishesh, Carole L Kapur, Reena Marcus, Sairam Mehra, Parthasarathy, Stuart F Quan","year":"2012","venue":"Journal of clinical sleep medicine","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cai et al., 2021] Xiyang Cai, Ziyu Jia, and Zehui Jiao. Two- stream squeeze-and-excitation network for multi-modal sleep staging. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1262- 1265. IEEE, 2021.","title":"Twostream squeeze-and-excitation network for multi-modal sleep staging","authors":"Xiyang Cai, Ziyu Jia, Zehui Jiao","year":"2021","venue":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chambon et al., 2018] Stanislas Chambon, Mathieu N Galtier, Pierrick J Arnal, Gilles Wainrib, and Alexandre Gramfort. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabil- itation Engineering, 26(4):758-769, 2018.","title":"A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series","authors":"Stanislas Chambon, N Mathieu, Galtier, J Pierrick, Gilles Arnal, Alexandre Wainrib, Gramfort","year":"2018","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Dian Chen, Dequan Wang, Trevor Dar- rell, and Sayna Ebrahimi. Contrastive test-time adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 295-305, 2022.","title":"Contrastive test-time adaptation","authors":"Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Jia et al., 2020a] Ziyu Jia, Xiyang Cai, Gaoxing Zheng, Jing Wang, and Youfang Lin. Sleepprintnet: A multi- variate multimodal neural network based on physiological time-series for automatic sleep staging. IEEE Transactions on Artificial Intelligence, 1(3):248-257, 2020.","title":"Sleepprintnet: A multivariate multimodal neural network based on physiological time-series for automatic sleep staging","authors":"Jia","year":"2020","venue":"IEEE Transactions on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jia et al., 2020b] Ziyu Jia, Youfang Lin, Jing Wang, Rong- hao Zhou, Xiaojun Ning, Yuanlai He, and Yaoshuai Zhao. Graphsleepnet: Adaptive spatial-temporal graph convolu- tional networks for sleep stage classification. In IJCAI, volume 2021, pages 1324-1330, 2020.","title":"Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification","authors":"Jia","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/184"},{"position":7,"raw":"[Jia et al., 2021a] Ziyu Jia, Youfang Lin, Jing Wang, Xiao- jun Ning, Yuanlai He, Ronghao Zhou, Yuhan Zhou, and H Lehman Li-wei. Multi-view spatial-temporal graph con- volutional networks with domain generalization for sleep stage classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:1977-1986, 2021.","title":"Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification","authors":"Jia","year":"2021","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jia et al., 2021b] Ziyu Jia, Youfang Lin, Jing Wang, Xue- hui Wang, Peiyi Xie, and Yingbin Zhang. Salientsleepnet: Multimodal salient wave detection network for sleep stag- ing. arXiv preprint arXiv:2105.13864, 2021.","title":"Salientsleepnet: Multimodal salient wave detection network for sleep staging","authors":"Jia","year":"2021","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2021/360"},{"position":9,"raw":"[Jia et al., 2022] Ziyu Jia, Xiyang Cai, and Zehui Jiao. Multi-modal physiological signals based squeeze-and- excitation network with domain adversarial learning for sleep staging. IEEE Sensors Journal, 22(4):3464-3471, 2022.","title":"Multi-modal physiological signals based squeeze-andexcitation network with domain adversarial learning for sleep staging","authors":"Ziyu Jia, Xiyang Cai, Zehui Jiao","year":"2022","venue":"IEEE Sensors Journal","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kemp et al., 2000] Bob Kemp, Aeilko H Zwinderman, Bert Tuk, Hilbert AC Kamphuisen, and Josefien JL Oberye. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg. IEEE Transactions on Biomedical Engineering, 47(9):1185-1194, 2000.","title":"Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg","authors":"Bob Kemp, Bert Aeilko H Zwinderman, Hilbert Tuk, Josefien Jl Ac Kamphuisen, Oberye","year":"2000","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Khalighi et al., 2016] Sirvan Khalighi, Teresa Sousa, José Moutinho Santos, and Urbano Nunes. Isruc-sleep: A comprehensive public dataset for sleep researchers. Computer Methods and Programs in Biomedicine, 124:180-192, 2016.","title":"Isruc-sleep: A comprehensive public dataset for sleep researchers","authors":"Sirvan Khalighi, Teresa Sousa, José Moutinho Santos, Urbano Nunes","year":"2016","venue":"Computer Methods and Programs in Biomedicine","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2023] Siyang Li, Ziwei Wang, Hanbin Luo, Lieyun Ding, and Dongrui Wu. T-time: Test-time informa- tion maximization ensemble for plug-and-play bcis. IEEE Transactions on Biomedical Engineering, 2023.","title":"T-time: Test-time information maximization ensemble for plug-and-play bcis","authors":"Siyang Li, Ziwei Wang, Hanbin Luo, Lieyun Ding, Dongrui Wu","year":"2023","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liang et al., 2023] Heng Liang, Yucheng Liu, Haichao Wang, and Ziyu Jia. Teacher assistant-based knowledge distillation extracting multi-level features on single chan- nel sleep eeg. In Proceedings of the Thirty-Second Inter- national Joint Conference on Artificial Intelligence, IJCAI, pages 3948-3956, 2023.","title":"Teacher assistant-based knowledge distillation extracting multi-level features on single channel sleep eeg","authors":"Heng Liang, Yucheng Liu, Haichao Wang, Ziyu Jia","year":"2023","venue":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/439"},{"position":14,"raw":"[Lim et al., 2023] Hyesu Lim, Byeonggeun Kim, Jaegul Choo, and Sungha Choi. Ttn: A domain-shift aware batch normalization in test-time adaptation. arXiv preprint arXiv:2302.05155, 2023.","title":"Ttn: A domain-shift aware batch normalization in test-time adaptation","authors":"Lim","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu and Jia, 2022] Yuchen Liu and Ziyu Jia. Bstt: A bayesian spatial-temporal transformer for sleep staging. In The Eleventh International Conference on Learning Rep- resentations, 2022.","title":"Bstt: A bayesian spatial-temporal transformer for sleep staging","authors":"Yuchen Liu, Ziyu Jia","year":"2022","venue":"The Eleventh International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2024] Chenyu Liu, Xinliang Zhou, Zhengri Zhu, Liming Zhai, Ziyu Jia, and Yang Liu. Vbh-gnn: Varia- tional bayesian heterogeneous graph neural networks for cross-subject emotion recognition. In The Twelfth Inter- national Conference on Learning Representations, 2024.","title":"Vbh-gnn: Variational bayesian heterogeneous graph neural networks for cross-subject emotion recognition","authors":"Chenyu Liu, Xinliang Zhou, Zhengri Zhu, Liming Zhai, Ziyu Jia, Yang Liu","year":"2024","venue":"The Twelfth International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ma et al., 2023] Shuo Ma, Yingwei Zhang, Yiqiang Chen, Tao Xie, Shuchao Song, and Ziyu Jia. Exploring struc- ture incentive domain adversarial learning for generaliz- able sleep stage classification. ACM Transactions on In- telligent Systems and Technology, 2023.","title":"Exploring structure incentive domain adversarial learning for generalizable sleep stage classification","authors":"Shuo Ma, Yingwei Zhang, Yiqiang Chen, Tao Xie, Shuchao Song, Ziyu Jia","year":"2023","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mousavi et al., 2019] Sajad Mousavi, Fatemeh Afghah, and U Rajendra Acharya. Sleepeegnet: Automated sleep stage scoring with sequence to sequence deep learning ap- proach. PloS one, 14(5):e0216456, 2019.","title":"Sleepeegnet: Automated sleep stage scoring with sequence to sequence deep learning approach","authors":"Sajad Mousavi, Fatemeh Afghah, U Rajendra, Acharya","year":"2019","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Niu et al., 2022] Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Guanghui Xu, Haokun Li, Peilin Zhao, Junzhou Huang, Yaowei Wang, and Mingkui Tan. Boost test-time performance with closed-loop inference. arXiv preprint arXiv:2203.10853, 2022.","title":"Boost test-time performance with closed-loop inference","authors":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Guanghui Xu, Haokun Li, Peilin Zhao, Junzhou Huang, Yaowei Wang, Mingkui Tan","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Niu et al., 2023] Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. Towards stable test-time adaptation in dynamic wild world. arXiv preprint arXiv:2302.12400, 2023.","title":"Towards stable test-time adaptation in dynamic wild world","authors":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Mingkui Tan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Perslev et al., 2019] Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, and Christian Igel. U-time: A fully convolutional network for time series segmentation applied to sleep staging. Advances in Neural Information Processing Systems, 32, 2019.","title":"U-time: A fully convolutional network for time series segmentation applied to sleep staging","authors":"Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Phan et al., 2019] Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y Chén, and Maarten De Vos. Se- qsleepnet: end-to-end hierarchical recurrent neural net- work for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(3):400-410, 2019.","title":"Seqsleepnet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging","authors":"Huy Phan, Fernando Andreotti, Navin Cooray, Maarten De Oliver Y Chén, Vos","year":"2019","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Phan et al., 2020] Huy Phan, Oliver Y Chén, Philipp Koch, Zongqing Lu, Ian McLoughlin, Alfred Mertins, and Maarten De Vos. Towards more accurate automatic sleep staging via deep transfer learning. IEEE Transactions on Biomedical Engineering, 68(6):1787-1798, 2020.","title":"Towards more accurate automatic sleep staging via deep transfer learning","authors":"Huy Phan, Philipp Oliver Y Chén, Zongqing Koch, Ian Lu, Alfred Mcloughlin, Maarten De Mertins, Vos","year":"2020","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Phan et al., 2021] Huy Phan, Oliver Y Chén, Minh C Tran, Philipp Koch, Alfred Mertins, and Maarten De Vos. Xsleepnet: Multi-view sequential model for automatic sleep staging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):5903-5915, 2021.","title":"Xsleepnet: Multi-view sequential model for automatic sleep staging","authors":"Huy Phan, Minh C Oliver Y Chén, Philipp Tran, Alfred Koch, Maarten De Mertins, Vos","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Quan et al., 1997] Stuart F Quan, Barbara V Howard, Con- rad Iber, James P Kiley, F Javier Nieto, George T O'Connor, David M Rapoport, Susan Redline, John Robbins, Jonathan M Samet, et al. The sleep heart health study: design, rationale, and methods. Sleep, 20(12):1077-1085, 1997.","title":"The sleep heart health study: design, rationale, and methods","authors":"Barbara V Stuart F Quan, Conrad Howard, Iber, Javier James P Kiley, George Nieto, T O' Connor, David M Rapoport, Susan Redline, John Robbins, Jonathan M Samet","year":"1997","venue":"Sleep","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shin et al., 2022] Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg, In So Kweon, and Kuk-Jin Yoon. Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 16928-16937, 2022.","title":"Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation","authors":"Shin","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sokolovsky et al., 2019] Michael Sokolovsky, Francisco Guerrero, Sarun Paisarnsrisomsuk, Carolina Ruiz, and Sergio A Alvarez. Deep learning for automated feature discovery and classification of sleep stages. IEEE/ACM transactions on computational biology and bioinformat- ics, 17(6):1835-1845, 2019.","title":"Deep learning for automated feature discovery and classification of sleep stages","authors":"Michael Sokolovsky, Francisco Guerrero, Sarun Paisarnsrisomsuk, Carolina Ruiz, Sergio A Alvarez","year":"2019","venue":"IEEE/ACM transactions on computational biology and bioinformatics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Supratak and Guo, 2020] Akara Supratak and Yike Guo. Tinysleepnet: An efficient deep learning model for sleep stage scoring based on raw single-channel eeg. In 2020 42nd Annual International Conference of the IEEE En- gineering in Medicine & Biology Society (EMBC), pages 641-644. IEEE, 2020.","title":"Tinysleepnet: An efficient deep learning model for sleep stage scoring based on raw single-channel eeg","authors":"Guo Supratak, Akara Supratak, Yike Guo","year":"2020","venue":"Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Supratak et al., 2017] Akara Supratak, Hao Dong, Chao Wu, and Yike Guo. Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engi- neering, 25(11):1998-2008, 2017.","title":"Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg","authors":"Akara Supratak, Hao Dong, Chao Wu, Yike Guo","year":"2017","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2020] Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726, 2020.","title":"Tent: Fully test-time adaptation by entropy minimization","authors":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2022] Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai. Continual test-time domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7201-7211, 2022.","title":"Continual test-time domain adaptation","authors":"Qin Wang, Olga Fink, Luc Van Gool, Dengxin Dai","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yang et al., 2022] Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, and Qi Dou. Dltta: Dynamic learning rate for test-time adapta- tion on cross-domain medical images. IEEE Transactions on Medical Imaging, 41(12):3575-3586, 2022.","title":"Dltta: Dynamic learning rate for test-time adaptation on cross-domain medical images","authors":"Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng, Ann Heng, Qi Dou","year":"2022","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yoo et al., 2021] Chaehwa Yoo, Hyang Woon Lee, and Je- Won Kang. Transferring structured knowledge in unsuper- vised domain adaptation of a sleep staging network. IEEE journal of biomedical and health informatics, 26(3):1273- 1284, 2021.","title":"Transferring structured knowledge in unsupervised domain adaptation of a sleep staging network","authors":"Yoo","year":"2021","venue":"IEEE journal of biomedical and health informatics","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhao et al., 2023] Bowen Zhao, Chen Chen, and Shu-Tao Xia. Delta: degradation-free fully test-time adaptation. arXiv preprint arXiv:2301.13018, 2023.","title":"Delta: degradation-free fully test-time adaptation","authors":"Bowen Zhao, Chen Chen, Shu-Tao Xia","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhou et al., 2023] Xinliang Zhou, Chenyu Liu, Liming Zhai, Ziyu Jia, Cuntai Guan, and Yang Liu. Interpretable and robust ai in eeg systems: A survey. arXiv preprint arXiv:2304.10755, 2023.","title":"Interpretable and robust ai in eeg systems: A survey","authors":"Xinliang Zhou, Chenyu Liu, Liming Zhai, Ziyu Jia, Cuntai Guan, Yang Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}