{"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/feature-weighting-and-regularization-of","title":"Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI","arxiv_id":null,"date":"2018-04-05","proceeding":"IEEE Signal Processing Letters 2018 4","authors":["Vasilisa Mishuhina","Xudong Jiang"],"abstract":"Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other. To extract the discriminative features and alleviate overfitting problem for motor imagery brain-computer interface (BCI), spatial filtering is widely applied but often only very few common spatial patterns (CSP) are selected as features while ignoring all others. However, using only few CSP features, though alleviates overfitting problem, loses the discriminating information, which limits the BCI performance. This letter proposes a novel feature weighting and regularization (FWR) method that utilizes all CSP features to avoid information loss. The proposed method can be applied in all CSP-based approaches. Experiments of this letter show the effect of the proposed method applied in the standard CSP and its two extensions, common spatio-spectral patterns and regularized CSP. Results on BCI Competition III Dataset IIIa and IV Dataset IIa demonstrate that the proposed FWR method enhances the classification accuracy comparing to the conventional feature selection approaches.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8331858","url_pdf":"https://www.researchgate.net/publication/324250137_Feature_Weighting_and_Regularization_of_Common_Spatial_Patterns_in_EEG-Based_Motor_Imagery_BCI","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":"feature-weighting-and-regularization-of","repo_url":"https://github.com/musicalka/fwr-csp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"motor-imagery","task_name":"Motor Imagery"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[{"slug":"bci-competition-datasets","name":"BCI Competition Datasets","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}