{"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/speech-imagery-bci-training-using-game-with-a","title":"Speech Imagery BCI Training Using Game with a Purpose","arxiv_id":null,"date":"2024-06-03","proceeding":"International Conference on Advanced Visual Interfaces 2024 6","authors":["Abdulrahman Mohamed Selim","Maurice Rekrut","Michael Barz","Daniel Sonntag"],"abstract":"Games are used in multiple fields of brain–computer interface (BCI) research and applications to improve participants’ engagement and enjoyment during electroencephalogram (EEG) data collection. However, despite potential benefits, no current studies have reported on implemented games for Speech Imagery BCI. Imagined speech is speech produced without audible sounds or active movement of the articulatory muscles. Collecting imagined speech EEG data is a time-consuming, mentally exhausting, and cumbersome process, which requires participants to read words off a computer screen and produce them as imagined speech. To improve this process for study participants, we implemented a maze-like game where a participant navigated a virtual robot capable of performing five actions that represented our words of interest while we recorded their EEG data. The study setup was evaluated with 15 participants. Based on their feedback, the game improved their engagement and enjoyment while resulting in a 69.10% average classification accuracy using a random forest classifier.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3656650.3656654","url_pdf":"https://www.dfki.de/fileadmin/user_upload/import/14962_avi2024-4.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":"speech-imagery-bci-training-using-game-with-a","repo_url":"https://github.com/AMSelim/Master_Thesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}