{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ssvep-dan-a-data-alignment-network-for-ssvep","title":"SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces","arxiv_id":"2311.12666","date":"2023-11-21","proceeding":null,"authors":["Sung-Yu Chen","Chi-Min Chang","Kuan-Jung Chiang","Chun-Shu Wei"],"abstract":"Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.","url_abs":"https://arxiv.org/abs/2311.12666v1","url_pdf":"https://arxiv.org/pdf/2311.12666v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ssvep-dan-a-data-alignment-network-for-ssvep","repo_url":"https://github.com/cecnl/ssvep-dan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"ssvep","task_name":"SSVEP"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}