{"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/comparative-evaluation-of-state-of-the-art","title":"Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs","arxiv_id":"1602.00904","date":"2016-02-02","proceeding":null,"authors":["Vangelis P. Oikonomou","Georgios Liaros","Kostantinos Georgiadis","Elisavet Chatzilari","Katerina Adam","Spiros Nikolopoulos","Ioannis Kompatsiaris"],"abstract":"Brain-computer interfaces (BCIs) have been gaining momentum in making\nhuman-computer interaction more natural, especially for people with\nneuro-muscular disabilities. Among the existing solutions the systems relying\non electroencephalograms (EEG) occupy the most prominent place due to their\nnon-invasiveness. However, the process of translating EEG signals into computer\ncommands is far from trivial, since it requires the optimization of many\ndifferent parameters that need to be tuned jointly. In this report, we focus on\nthe category of EEG-based BCIs that rely on Steady-State-Visual-Evoked\nPotentials (SSVEPs) and perform a comparative evaluation of the most promising\nalgorithms existing in the literature. More specifically, we define a set of\nalgorithms for each of the various different parameters composing a BCI system\n(i.e. filtering, artifact removal, feature extraction, feature selection and\nclassification) and study each parameter independently by keeping all other\nparameters fixed. The results obtained from this evaluation process are\nprovided together with a dataset consisting of the 256-channel, EEG signals of\n11 subjects, as well as a processing toolbox for reproducing the results and\nsupporting further experimentation. In this way, we manage to make available\nfor the community a state-of-the-art baseline for SSVEP-based BCIs that can be\nused as a basis for introducing novel methods and approaches.","url_abs":"http://arxiv.org/abs/1602.00904v2","url_pdf":"http://arxiv.org/pdf/1602.00904v2.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":"comparative-evaluation-of-state-of-the-art","repo_url":"https://github.com/MAMEM/ssvep-eeg-processing-toolbox","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"comparative-evaluation-of-state-of-the-art","repo_url":"https://github.com/MAMEM/eeg-processing-toolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"comparative-evaluation-of-state-of-the-art","repo_url":"https://github.com/akhilmurali013/project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"ssvep","task_name":"SSVEP"},{"task_slug":"feature-selection","task_name":"feature selection"}],"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}