{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:00:41Z","timestamp":1777564841985,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["QYZDY-SSW-JSC005"],"award-info":[{"award-number":["QYZDY-SSW-JSC005"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>The brain\u2013computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 \u00b1 6.18% and 80.58 \u00b1 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided.<\/jats:p>","DOI":"10.3390\/brainsci13050780","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T03:22:19Z","timestamp":1683775339000},"page":"780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7421-5552","authenticated-orcid":false,"given":"Dongcen","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengzhen","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xisheng","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TRE.2000.847807","article-title":"Brain-computer interface technology: A review of the first international meeting","volume":"8","author":"Wolpaw","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/86.847808","article-title":"The mental prosthesis: Assessing the speed of a P300-based brain-computer interface","volume":"8","author":"Donchin","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jneumeth.2012.04.011","article-title":"Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard","volume":"208","author":"Hwang","year":"2012","journal-title":"J. Neurosci. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.robot.2012.11.002","article-title":"Assisted navigation for a brain-actuated intelligent wheelchair","volume":"61","author":"Lopes","year":"2013","journal-title":"Robot. Auton. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MRA.2012.2229936","article-title":"Brain-controlled wheelchairs: A robotic architecture","volume":"20","author":"Carlson","year":"2013","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, D., Tong, Y., Dong, X., Wang, C., Huo, L., Li, Y., Zhang, Q., and Feng, X. (2021, January 27\u201331). A brain-computer interface based semi-autonomous robotic system. Proceedings of the 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China.","DOI":"10.1109\/ROBIO54168.2021.9739367"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"026012","DOI":"10.1088\/1741-2552\/aaf594","article-title":"Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm","volume":"16","author":"Chen","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_8","unstructured":"Peining, P., Tan, G., and Wai, A.A.P. (2017, January 10\u201311). Evaluation of consumer-grade EEG headsets for BCI drone control. Proceedings of the IRC Conference on Science, Engineering, and Technology, Singapore."},{"key":"ref_9","unstructured":"Holm, N.S., and Puthusserypady, S. (2019, January 18\u201320). An improved five class MI based BCI scheme for drone control using filter bank CSP. Proceedings of the 2019 7th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Republic of Korea."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TSMCC.2012.2226444","article-title":"A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks","volume":"42","author":"Frisoli","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"16","DOI":"10.24075\/brsmu.2016-02-02","article-title":"Preliminary results of a controlled study of BCI\u2013exoskeleton technology efficacy in patients with poststroke arm paresis","volume":"2","author":"Frolov","year":"2016","journal-title":"Bull. Russ. State Med. Univ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/JPROC.2015.2419736","article-title":"Towards independence: A BCI telepresence robot for people with severe motor disabilities","volume":"103","author":"Leeb","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"68","DOI":"10.3389\/fnhum.2017.00068","article-title":"Reaching and grasping a glass of water by locked-in ALS patients through a BCI-controlled humanoid robot","volume":"11","author":"Spataro","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/aaf12e","article-title":"A comprehensive review of EEG-based brain\u2013computer interface paradigms","volume":"16","author":"Abiri","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/0013-4694(88)90149-6","article-title":"Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials","volume":"70","author":"Farwell","year":"1988","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/0013-4694(91)90040-B","article-title":"An EEG-based brain-computer interface for cursor control","volume":"78","author":"Wolpaw","year":"1991","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TBME.2002.803536","article-title":"Design and implementation of a brain-computer interface with high transfer rates","volume":"49","author":"Cheng","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ba\u015far, E. (1988). Dynamics of Sensory and Cognitive Processing by the Brain, Springer.","DOI":"10.1007\/978-3-642-71531-0"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TNSRE.2006.875576","article-title":"A practical VEP-based brain-computer interface","volume":"14","author":"Wang","year":"2006","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain computer interfaces, a review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9087","DOI":"10.1109\/JSEN.2022.3161743","article-title":"Enhancement of SSVEPs classification in BCI-based wearable instrumentation through machine learning techniques","volume":"22","author":"Apicella","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, D., Tang, F., Li, Y., Zhang, Q., and Feng, X. (2023). An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey. Brain Sci., 13.","DOI":"10.3390\/brainsci13030483"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alzahab, N.A., Apollonio, L., Di Iorio, A., Alshalak, M., Iarlori, S., Ferracuti, F., Monteri\u00f9, A., and Porcaro, C. (2021). Hybrid deep learning (hDL)-based brain-computer interface (BCI) systems: A systematic review. Brain Sci., 11.","DOI":"10.3390\/brainsci11010075"},{"key":"ref_24","unstructured":"Zhang, X., Yao, L., Wang, X., Monaghan, J., Mcalpine, D., and Zhang, Y. (2019). A survey on deep learning based brain computer interface: Recent advances and new frontiers. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kwak, N.-S., M\u00fcller, K.-R., and Lee, S.-W. (2017). A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0172578"},{"key":"ref_27","unstructured":"Aznan, N.K.N., Bonner, S., Connolly, J., Al Moubayed, N., and Breckon, T. (2018, January 7\u201310). On the classification of SSVEP-based dry-EEG signals via convolutional neural networks. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105042","DOI":"10.1016\/j.compbiomed.2021.105042","article-title":"A CNN-based multi-target fast classification method for AR-SSVEP","volume":"141","author":"Zhao","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TNSRE.2019.2904791","article-title":"On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications","volume":"27","author":"Podmore","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/ACCESS.2018.2886759","article-title":"A single-channel SSVEP-based BCI speller using deep learning","volume":"7","author":"Nguyen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","first-page":"244","article-title":"MHLCNN: Multi-harmonic linkage CNN model for SSVEP and SSMVEP signal classification","volume":"69","author":"Dang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"026028","DOI":"10.1088\/1741-2552\/ab6a67","article-title":"Comparing user-dependent and user-independent training of CNN for SSVEP BCI","volume":"17","author":"Ravi","year":"2020","journal-title":"J. Neural Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"046008","DOI":"10.1088\/1741-2560\/12\/4\/046008","article-title":"Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain\u2013computer interface","volume":"12","author":"Chen","year":"2015","journal-title":"J. Neural Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TNSRE.2021.3132162","article-title":"Filter bank convolutional neural network for short time-window steady-state visual evoked potential classification","volume":"29","author":"Ding","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"147129","DOI":"10.1109\/ACCESS.2021.3124238","article-title":"Filter bank convolutional neural network for SSVEP classification","volume":"9","author":"Zhao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"056014","DOI":"10.1088\/1741-2552\/ac8dc5","article-title":"An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition","volume":"19","author":"Pan","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, Y., Pan, Y., Xu, P., and Guan, C. (2022). A Transformer-based deep neural network model for SSVEP classification. arXiv.","DOI":"10.1016\/j.neunet.2023.04.045"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109674","DOI":"10.1016\/j.jneumeth.2022.109674","article-title":"FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection","volume":"379","author":"Yao","year":"2022","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"035018","DOI":"10.1088\/2057-1976\/ac6300","article-title":"FBDNN: Filter banks and deep neural networks for portable and fast brain-computer interfaces","volume":"8","author":"Bassi","year":"2022","journal-title":"Biomed. Phys. Eng. Express"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nakanishi, M., Wang, Y., Wang, Y.-T., and Jung, T.-P. (2015). A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140703"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1109\/TNSRE.2016.2627556","article-title":"A benchmark dataset for SSVEP-based brain\u2013computer interfaces","volume":"25","author":"Wang","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1109\/TBME.2006.886577","article-title":"Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs","volume":"53","author":"Lin","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_43","unstructured":"Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Giusti, E., Ghio, S., Oveis, A.H., and Martorella, M. (2022). Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14184665"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20043","DOI":"10.1109\/ACCESS.2021.3128611","article-title":"Imaginary control of a mobile vehicle using deep learning algorithm: A brain computer interface study","volume":"10","author":"Hekmatmanesh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TBME.2013.2270283","article-title":"A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control","volume":"60","author":"Li","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hayta, \u00dc., Irimia, D.C., Guger, C., Erkutlu, \u0130., and G\u00fczelbey, \u0130.H. (2022). Optimizing Motor Imagery Parameters for Robotic Arm Control by Brain-Computer Interface. Brain Sci., 12.","DOI":"10.3390\/brainsci12070833"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/5\/780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:28Z","timestamp":1760124748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/5\/780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["brainsci13050780"],"URL":"https:\/\/doi.org\/10.3390\/brainsci13050780","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}