{"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/intracerebral-eeg-artifact-identification","title":"Intracerebral EEG Artifact Identification Using Convolutional Neural Networks","arxiv_id":null,"date":"2018-08-13","proceeding":"Neuroinformatics 2018 8","authors":["Petr Nejedly","Jan Cimbalnik","Petr Klimes","Filip Plesinger","Josef Halamek","Vaclav Kremen","Ivo Viscor","Benjamin H. Brinkmann","Martin Pail","Milan Brazdil","Gregory Worrell","Pavel Jurak"],"abstract":"Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method’s performance against expert annotations. The method was trained and tested on data obtained from St Anne’s University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.","url_abs":"https://doi.org/10.1007/s12021-018-9397-6","url_pdf":"https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.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":"intracerebral-eeg-artifact-identification","repo_url":"https://github.com/xnejed07/NoiseDetectionCNN","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"artifact-detection","task_name":"Artifact Detection"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg-artifact-removal","task_name":"EEG Artifact Removal"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}