{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:12:24Z","timestamp":1760235144136,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.<\/jats:p>","DOI":"10.3390\/s21155019","type":"journal-article","created":{"date-parts":[[2021,7,25]],"date-time":"2021-07-25T22:07:00Z","timestamp":1627250820000},"page":"5019","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-4788","authenticated-orcid":false,"given":"Yeou-Jiunn","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan"}]},{"given":"Pei-Chung","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-1080","authenticated-orcid":false,"given":"Shih-Chung","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9075-2350","authenticated-orcid":false,"given":"Chung-Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Intelligent Robotics Engineering, Kun-Shan University, Tainan 710303, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.rehab.2017.09.004","article-title":"Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral sclerosis","volume":"61","author":"Guy","year":"2018","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3389\/fnhum.2018.00040","article-title":"Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG","volume":"12","author":"Juel","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/ACCESS.2015.2466110","article-title":"A wireless augmentative and al-ternative communication system for people with speech disabilities","volume":"3","author":"Hornero","year":"2015","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TBCAS.2017.2757031","article-title":"A Low-Power Wearable Stand-Alone Tongue Drive System for People With Severe Disabilities","volume":"12","author":"Jafari","year":"2017","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Anila, M., and Radhika, P. (2017, January 22\u201324). Lip contour detection based AAC device using Morse code. Proceedings of the International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8299950"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Garcia, R.G., Ibarra, J.B.G., Paglinawan, C.C., Paglinawan, A.C., Valiente, L., Sejera, M.M., Bernal, M.V., Cortinas, W.J., Dave, J.M., and Villegas, M.C. (2017, January 1\u20133). Wearable augmentative and alternative communication device for paralysis victims using brute force algorithm for pattern recognition. Proceedings of the IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, Manila, Philippines.","DOI":"10.1109\/HNICEM.2017.8269554"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Radici, E., Bonacina, S., and Leo, G.D. (2016, January 17\u201320). Design and development of an AAC app based on a speech-to-symbol technology. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591256"},{"key":"ref_8","first-page":"53","article-title":"Access to augmentative and alternative communication: New technologies and clinical decision-making","volume":"5","author":"Fager","year":"2012","journal-title":"J. Pediatr. Rehabil. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rehab.2014.11.002","article-title":"Brain-Machine Interface (BMI) in paralysis","volume":"58","author":"Chaudhary","year":"2015","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Velasco-\u00c1lvarez, F., Fern\u00e1ndez-Rodr\u00edguez, \u00c1., Vizca\u00edno-Mart\u00edn, F.-J., D\u00edaz-Estrella, A., and Ron-Angevin, R. (2021). Brain\u2013Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications. Sensors, 21.","DOI":"10.3390\/s21113716"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"E6058","DOI":"10.1073\/pnas.1508080112","article-title":"High-speed spelling with a noninvasive brain-computer in-terface","volume":"112","author":"Chen","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tan, P., Tan, G., and Cai, Z. (2015, January 15\u201317). Dual-tree complex wavelet transform-based feature extraction for brain computer interface. Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China.","DOI":"10.1109\/FSKD.2015.7382102"},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.pneurobio.2009.11.005","article-title":"Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives","volume":"90","author":"Vialatte","year":"2010","journal-title":"Prog. Neurobiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kanoga, S., Nakanishi, M., Murai, A., Tada, M., and Kanemura, A. (2018, January 17\u201321). Semi-simulation experiments for quantifying the performance of SSVEP-based BCI after reducing artifacts from trapezius muscles. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513180"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ming, G., Wang, Y., Pei, W., and Chen, H. (2019, January 20\u201323). Optimizing spatial contrast of a new checkerboard stimulus for eliciting robust SSVEPs. Proceedings of the 9th International IEEE\/EMBS Conference on Neural Engineering, San Francisco, CA, USA.","DOI":"10.1109\/NER.2019.8716972"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Park, J., Park, J., Shin, D., and Choi, Y. (2021). A BCI Based Alerting System for Attention Recovery of UAV Operators. Sensors, 21.","DOI":"10.3390\/s21072447"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TASLP.2017.2718843","article-title":"Denoised Bottleneck Features From Deep Autoencoders for Telephone Conversation Analysis","volume":"25","author":"Janod","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_20","first-page":"515","article-title":"The Speech Enhancement via Attention Masking Network (SEAMNET): An End-to-end System for Joint Suppression of Noise and Reverberation","volume":"29","author":"Borgstrom","year":"2020","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/TBME.2014.2320948","article-title":"A Dynamically Optimized SSVEP Brain\u2013Computer Interface (BCI) Speller","volume":"62","author":"Yin","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5019\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:34:11Z","timestamp":1760164451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5019"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":22,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21155019"],"URL":"https:\/\/doi.org\/10.3390\/s21155019","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}