{"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/boosting-factor-specific-functional","title":"Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals","arxiv_id":"1609.06070","date":"2016-09-20","proceeding":null,"authors":["David Rügamer","Sarah Brockhaus","Kornelia Gentsch","Klaus Scherer","Sonja Greven"],"abstract":"The link between different psychophysiological measures during emotion\nepisodes is not well understood. To analyse the functional relationship between\nelectroencephalography (EEG) and facial electromyography (EMG), we apply\nhistorical function-on-function regression models to EEG and EMG data that were\nsimultaneously recorded from 24 participants while they were playing a\ncomputerised gambling task. Given the complexity of the data structure for this\napplication, we extend simple functional historical models to models including\nrandom historical effects, factor-specific historical effects, and\nfactor-specific random historical effects. Estimation is conducted by a\ncomponent-wise gradient boosting algorithm, which scales well to large data\nsets and complex models.","url_abs":"http://arxiv.org/abs/1609.06070v2","url_pdf":"http://arxiv.org/pdf/1609.06070v2.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":"boosting-factor-specific-functional","repo_url":"https://github.com/davidruegamer/BoostingSignalSynchro","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"electromyography-emg","task_name":"Electromyography (EMG)"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1609.06070","atlas_url":"https://app.syntology.ai/?focus=1609.06070","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}