{"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/fingerflex-inferring-finger-trajectories-from","title":"FingerFlex: Inferring Finger Trajectories from ECoG signals","arxiv_id":"2211.01960","date":"2022-10-23","proceeding":null,"authors":["Vladislav Lomtev","Alexander Kovalev","Alexey Timchenko"],"abstract":"Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger movement regression on electrocorticographic (ECoG) brain data. State-of-the-art performance was achieved on a publicly available BCI competition IV dataset 4 with a correlation coefficient between true and predicted trajectories up to 0.74. The presented method provides the opportunity for developing fully-functional high-precision cortical motor brain-computer interfaces.","url_abs":"https://arxiv.org/abs/2211.01960v2","url_pdf":"https://arxiv.org/pdf/2211.01960v2.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":"fingerflex-inferring-finger-trajectories-from","repo_url":"https://github.com/Irautak/FingerFlex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"brain-decoding","task_name":"Brain Decoding"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-decoding-on-bci-competition-iv-ecog-to","task":"Brain Decoding","dataset":"BCI Competition IV: ECoG to Finger Movements","model":"FingerFlex","rank_in_archive_order":1,"of":7,"metrics":{"Pearson Correlation":"0.67"},"uses_additional_data":false},{"leaderboard":"/sota/brain-decoding-on-stanford-ecog-library-ecog","task":"Brain Decoding","dataset":"Stanford ECoG library: ECoG to Finger Movements","model":"FingerFlex","rank_in_archive_order":1,"of":1,"metrics":{"Pearson Correlation":"0.49"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}