{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T10:23:32Z","timestamp":1785493412653,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Key Research and Development Program of Zhejiang Province","award":["2022C03029"],"award-info":[{"award-number":["2022C03029"]}]},{"name":"The Key Research and Development Program of Zhejiang Province","award":["2021J034"],"award-info":[{"award-number":["2021J034"]}]},{"name":"The Key Research and Development Program of Zhejiang Province","award":["2022Z147"],"award-info":[{"award-number":["2022Z147"]}]},{"name":"The Key Research and Development Program of Zhejiang Province","award":["2020Z082"],"award-info":[{"award-number":["2020Z082"]}]},{"name":"The Key Research and Development Program of Zhejiang Province","award":["2022J042"],"award-info":[{"award-number":["2022J042"]}]},{"name":"The Natural Science Foundation of Ningbo","award":["2022C03029"],"award-info":[{"award-number":["2022C03029"]}]},{"name":"The Natural Science Foundation of Ningbo","award":["2021J034"],"award-info":[{"award-number":["2021J034"]}]},{"name":"The Natural Science Foundation of Ningbo","award":["2022Z147"],"award-info":[{"award-number":["2022Z147"]}]},{"name":"The Natural Science Foundation of Ningbo","award":["2020Z082"],"award-info":[{"award-number":["2020Z082"]}]},{"name":"The Natural Science Foundation of Ningbo","award":["2022J042"],"award-info":[{"award-number":["2022J042"]}]},{"name":"The Key Research and Development Program of Ningbo","award":["2022C03029"],"award-info":[{"award-number":["2022C03029"]}]},{"name":"The Key Research and Development Program of Ningbo","award":["2021J034"],"award-info":[{"award-number":["2021J034"]}]},{"name":"The Key Research and Development Program of Ningbo","award":["2022Z147"],"award-info":[{"award-number":["2022Z147"]}]},{"name":"The Key Research and Development Program of Ningbo","award":["2020Z082"],"award-info":[{"award-number":["2020Z082"]}]},{"name":"The Key Research and Development Program of Ningbo","award":["2022J042"],"award-info":[{"award-number":["2022J042"]}]},{"name":"\u201cScience and Technology Innovation 2025\u201d Major Special Project of Ningbo","award":["2022C03029"],"award-info":[{"award-number":["2022C03029"]}]},{"name":"\u201cScience and Technology Innovation 2025\u201d Major Special Project of Ningbo","award":["2021J034"],"award-info":[{"award-number":["2021J034"]}]},{"name":"\u201cScience and Technology Innovation 2025\u201d Major Special Project of Ningbo","award":["2022Z147"],"award-info":[{"award-number":["2022Z147"]}]},{"name":"\u201cScience and Technology Innovation 2025\u201d Major Special Project of Ningbo","award":["2020Z082"],"award-info":[{"award-number":["2020Z082"]}]},{"name":"\u201cScience and Technology Innovation 2025\u201d Major Special Project of Ningbo","award":["2022J042"],"award-info":[{"award-number":["2022J042"]}]},{"name":"Young Doctor Innovation Research Project of Ningbo Natural Science Foundation","award":["2022C03029"],"award-info":[{"award-number":["2022C03029"]}]},{"name":"Young Doctor Innovation Research Project of Ningbo Natural Science Foundation","award":["2021J034"],"award-info":[{"award-number":["2021J034"]}]},{"name":"Young Doctor Innovation Research Project of Ningbo Natural Science Foundation","award":["2022Z147"],"award-info":[{"award-number":["2022Z147"]}]},{"name":"Young Doctor Innovation Research Project of Ningbo Natural Science Foundation","award":["2020Z082"],"award-info":[{"award-number":["2020Z082"]}]},{"name":"Young Doctor Innovation Research Project of Ningbo Natural Science Foundation","award":["2022J042"],"award-info":[{"award-number":["2022J042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In motor imagery (MI) brain\u2013computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient\u2019s demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain\u2019s force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time\u2013frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time\u2013frequency\u2013space domain features to improve the algorithm\u2019s sensitivity. The results showed that the accuracy of the algorithm was 86.4 \u00b1 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 \u00b1 14.5%), Deep ConvNet (75.3 \u00b1 12.3%), Shallow ConvNet (77.6 \u00b1 11.8%), EEGNet (82.3 \u00b1 13.8%), and SCNN-BiLSTM (69.1 \u00b1 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.<\/jats:p>","DOI":"10.3390\/e25030464","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T03:11:39Z","timestamp":1678158699000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements"],"prefix":"10.3390","volume":"25","author":[{"given":"Junpeng","family":"Sheng","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China"},{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2456-3567","authenticated-orcid":false,"given":"Jialin","family":"Xu","sequence":"additional","affiliation":[{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China"},{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimeng","family":"You","sequence":"additional","affiliation":[{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2499-7397","authenticated-orcid":false,"given":"Guokun","family":"Zuo","sequence":"additional","affiliation":[{"name":"Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-444-63934-9.00001-9","article-title":"Human brain function and brain-computer interfaces","volume":"168","author":"Ramsey","year":"2020","journal-title":"Handb. Clin. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, K., Tian, F., Xu, M., Zhang, S., Xu, L., and Ming, D. (2022). Resting-state EEG in alpha rhythm may be indicative of the performance of motor imagery-based brain-computer interface. Entropy, 24.","DOI":"10.3390\/e24111556"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128","DOI":"10.3389\/fnhum.2019.00128","article-title":"Exploring training effect in 42 human subjects using a non-invasive sensorimotor rhythm based online BCI","volume":"13","author":"Meng","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, C., Phua, K.S., Ang, K.K., Guan, C., Zhang, H., Lin, R.S., Chua, K.S.G., Ang, B.T., and Kuah, C.W.K. (May, January 29). A feasibility study of non-invasive motor-imagery BCI-based robotic rehabilitation for stroke patients. Proceedings of the 2009 4th International IEEE\/EMBS Conference on Neural Engineering, Antalya, Turkey.","DOI":"10.1109\/NER.2009.5109285"},{"key":"ref_5","unstructured":"Ang, K.K., Guan, C., Chua, K.S.G., Ang, B.T., Kuah, C., Wang, C., Phua, K.S., Chin, Z.Y., and Zhang, H. (September, January 31). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"036005","DOI":"10.1088\/1741-2560\/8\/3\/036005","article-title":"Closing the sensorimotor loop: Haptic feedback facilitates decoding of motor imagery","volume":"8","author":"Peters","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1177\/1550059414522229","article-title":"A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke","volume":"46","author":"Ang","year":"2015","journal-title":"Clin. EEG Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6132","DOI":"10.4081\/ejtm.2016.6132","article-title":"Paired associative stimulation using brain-computer interfaces for stroke rehabilitation: A pilot study","volume":"26","author":"Cho","year":"2016","journal-title":"Eur. J. Transl. Myol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"88","DOI":"10.5772\/45703","article-title":"Robot-aided upper-limb rehabilitation based on motor imagery EEG","volume":"8","author":"Xu","year":"2011","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"046003","DOI":"10.1088\/1741-2560\/10\/4\/046003","article-title":"Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface","volume":"10","author":"LaFleur","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/1743-0003-10-106","article-title":"EEG feature comparison and classification of simple and compound limb motor imagery","volume":"10","author":"Yi","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_12","first-page":"1007","article-title":"Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose","volume":"52","author":"Bhattacharyya","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TNSRE.2016.2627058","article-title":"Quantitative EEG evaluation during robot-assisted foot movement","volume":"25","author":"Formaggio","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"420561","DOI":"10.1155\/2014\/420561","article-title":"Enhanced performance by time-frequency-phase feature for EEG-based BCI systems","volume":"2014","author":"Xu","year":"2014","journal-title":"Sci. World J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, Z., Guo, Y., He, F., Qi, H., Xu, M., and Ming, D. (2017, January 25\u201328). An EEG study on hand force imagery for brain-computer interfaces. Proceedings of the 2017 8th International IEEE\/EMBS Conference on Neural Engineering (NER), Shanghai, China.","DOI":"10.1109\/NER.2017.8008439"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, K., Xu, M., Zhang, S., Ke, Y., and Ming, D. (2018, January 18\u201321). Analysis and classification for EEG patterns of force motor imagery using movement related cortical potentials. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512184"},{"key":"ref_17","unstructured":"Yang, H., Sakhavi, S., Ang, K.K., and Guan, C. (2015, January 25\u201329). On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1109\/TNSRE.2022.3154369","article-title":"Time-distributed attention network for EEG-based motor imagery decoding from the same limb","volume":"30","author":"Ma","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.bspc.2022.103496","article-title":"An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces","volume":"74","author":"Roy","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s12984-016-0119-8","article-title":"EEG oscillatory patterns and classification of sequential compound limb motor imagery","volume":"13","author":"Yi","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","article-title":"Event-related EEG\/MEG synchronization and desynchronization: Basic principles","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tang, Z., Sun, S., Zhang, S., Chen, Y., Li, C., and Chen, S. (2016). A Brain-Machine Interface Based on ERD\/ERS for an Upper-Limb Exoskeleton Control. Sensors, 16.","DOI":"10.3390\/s16122050"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_25","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_26","unstructured":"Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_27","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_32","unstructured":"Wu, W., Gao, X., and Gao, S. (2006, January 17\u201318). One-versus-the-rest(OVR) algorithm: An extension of common spatial patterns(CSP) algorithm to multi-class case. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6613105","DOI":"10.1155\/2021\/6613105","article-title":"A novel time-incremental end-to-end shared neural network with attention-based feature fusion for multiclass motor imagery recognition","volume":"2021","author":"Lian","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_36","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/3\/464\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:49:42Z","timestamp":1760122182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/3\/464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["e25030464"],"URL":"https:\/\/doi.org\/10.3390\/e25030464","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}