{"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/a-knowledge-distillation-framework-for","title":"A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data","arxiv_id":"2211.02638","date":"2022-10-27","proceeding":null,"authors":["Mithunjha Anandakumar","Jathurshan Pradeepkumar","Simon L. Kappel","Chamira U. S. Edussooriya","Anjula C. De Silva"],"abstract":"Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.","url_abs":"https://arxiv.org/abs/2211.02638v1","url_pdf":"https://arxiv.org/pdf/2211.02638v1.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":"a-knowledge-distillation-framework-for","repo_url":"https://github.com/Mithunjha/EarEEG_KnowledgeDistillation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg-based-sleep-staging","task_name":"EEG based sleep staging"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"sleep-quality","task_name":"Sleep Quality"},{"task_slug":"sleep-staging","task_name":"Sleep Staging"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}