{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T07:15:20Z","timestamp":1765178120518,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"An Nan Hospital, China Medical University","award":["ANHRF111-26","ANHRF110-22"],"award-info":[{"award-number":["ANHRF111-26","ANHRF110-22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain\u2013computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.<\/jats:p>","DOI":"10.3390\/s22218303","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"8303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain\u2013Computer Interfaces"],"prefix":"10.3390","volume":"22","author":[{"given":"Chia-Chun","family":"Chuang","sequence":"first","affiliation":[{"name":"Department of Anesthesia, An Nan Hospital, China Medical University, Tainan 70965, Taiwan"},{"name":"Department of Medical Sciences Industry, Chang Jung Christian University, Tainan 71101, Taiwan"}]},{"given":"Chien-Ching","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Anesthesia, An Nan Hospital, China Medical University, Tainan 70965, Taiwan"},{"name":"Department of Medical Sciences Industry, Chang Jung Christian University, Tainan 71101, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2480-8843","authenticated-orcid":false,"given":"Edmund-Cheung","family":"So","sequence":"additional","affiliation":[{"name":"Department of Anesthesia, An Nan Hospital, China Medical University, Tainan 70965, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1900-2199","authenticated-orcid":false,"given":"Chia-Hong","family":"Yeng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-4788","authenticated-orcid":false,"given":"Yeou-Jiunn","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41952","DOI":"10.1109\/ACCESS.2022.3164075","article-title":"Eye-Tracking Assistive Technologies for Individuals with Amyotrophic Lateral Sclerosis","volume":"10","author":"Edughele","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roberts, B., Theunissen, F., Mastaglia, F.L., Akkari, P.A., and Flynn, L.L. (2022). Synucleinopathy in Amyotrophic Lateral Sclerosis: A Potential Avenue for Antisense Therapeutics. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23169364"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TNSRE.2020.2980772","article-title":"Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System","volume":"28","author":"Borgheai","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Chen, Y.J., Chen, S.C., Zaeni, I.A.E., and Wu, C.M. (2016). Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System. Appl. Sci., 6.","DOI":"10.3390\/app6100270"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/TNSRE.2017.2666479","article-title":"Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI with a Single Flicker Frequency","volume":"25","author":"Maye","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s40815-016-0289-3","article-title":"A Single Channel SSVEP based BCI with a Fuzzy Feature Threshold Algorithm in a Maze Game","volume":"19","author":"Chen","year":"2017","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"M1","DOI":"10.1149\/2.0071701jss","article-title":"High On\/Off Ratio Field-Effect Transistor Based on Semiconducting Single-Walled Carbon Nanotubes by Selective Separation","volume":"6","author":"Young","year":"2017","journal-title":"ECS J. Solid State Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neunet.2018.02.011","article-title":"Towards correlation-based time window selection method for motor imagery BCIs","volume":"102","author":"Feng","year":"2018","journal-title":"Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TBME.2018.2889705","article-title":"Riemannian Procrustes Analysis: Transfer Learning for Brain\u2013Computer Interfaces","volume":"66","author":"Rodrigues","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4107","DOI":"10.1109\/TCOMM.2022.3170988","article-title":"Unsupervised Learning Discriminative MIG Detectors in Nonhomogeneous Clutter","volume":"70","author":"Hua","year":"2022","journal-title":"IEEE Trans. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s00542-019-04654-2","article-title":"Convolutional Denoising Autoencoder based SSVEP Signal Enhancement to SSVEP-based BCIs","volume":"28","author":"Chuang","year":"2022","journal-title":"Microsyst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/S0140-6736(10)61156-7","article-title":"Amyotrophic lateral sclerosis","volume":"377","author":"Kiernan","year":"2011","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"407","DOI":"10.5405\/jmbe.765","article-title":"Brain-computer Interface Based on Visual Evoked Potentials to Command Autonomous Robotic Wheelchair","volume":"30","author":"Muller","year":"2010","journal-title":"J. Med. Biol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"595890","DOI":"10.3389\/fnhum.2020.595890","article-title":"SSVEP BCI and Eye Tracking Use by Individuals with Late-Stage ALS and Visual Impairments","volume":"20","author":"Peters","year":"2020","journal-title":"Front. Hum. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khok, H.J., Koh, V.T.C., and Guan, C. (2020, January 11\u201314). Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9283310"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TNSRE.2015.2496184","article-title":"Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups","volume":"24","author":"Hsu","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cheng, M., Jiao, L., Yan, P., Gu, H., Sun, J., Qiu, T., and Wang, X. (2022). A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230. Sensors, 22.","DOI":"10.3390\/s22134943"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.1109\/TSP.2021.3122303","article-title":"Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning","volume":"69","author":"Bazerque","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","article-title":"HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition","volume":"41","author":"Ranjan","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/TBME.2014.2320948","article-title":"A dynamically optimized SSVEP Brain-computer interface (BCI) Speller","volume":"62","author":"Yin","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:36Z","timestamp":1760144736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,29]]},"references-count":21,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218303"],"URL":"https:\/\/doi.org\/10.3390\/s22218303","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,29]]}}}