{"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/mining-within-trial-oscillatory-brain","title":"Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters","arxiv_id":"1804.10454","date":"2018-04-27","proceeding":null,"authors":["Andreas Meinel","Henrich Kolkhorst","Michael Tangermann"],"abstract":"Data-driven spatial filtering algorithms optimize scores such as the contrast\nbetween two conditions to extract oscillatory brain signal components. Most\nmachine learning approaches for filter estimation, however, disregard\nwithin-trial temporal dynamics and are extremely sensitive to changes in\ntraining data and involved hyperparameters. This leads to highly variable\nsolutions and impedes the selection of a suitable candidate for,\ne.g.,~neurotechnological applications. Fostering component introspection, we\npropose to embrace this variability by condensing the functional signatures of\na large set of oscillatory components into homogeneous clusters, each\nrepresenting specific within-trial envelope dynamics.\n  The proposed method is exemplified by and evaluated on a complex hand force\ntask with a rich within-trial structure. Based on electroencephalography data\nof 18 healthy subjects, we found that the components' distinct temporal\nenvelope dynamics are highly subject-specific. On average, we obtained seven\nclusters per subject, which were strictly confined regarding their underlying\nfrequency bands. As the analysis method is not limited to a specific spatial\nfiltering algorithm, it could be utilized for a wide range of\nneurotechnological applications, e.g., to select and monitor functionally\nrelevant features for brain-computer interface protocols in stroke\nrehabilitation.","url_abs":"http://arxiv.org/abs/1804.10454v2","url_pdf":"http://arxiv.org/pdf/1804.10454v2.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":"mining-within-trial-oscillatory-brain","repo_url":"https://github.com/bsdlab/func_mining","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"}],"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}