{"id":19252315,"url":"https://github.com/sccn/std_clust2ch","last_synced_at":"2025-02-23T16:50:52.445Z","repository":{"id":150759862,"uuid":"327721584","full_name":"sccn/std_clust2ch","owner":"sccn","description":null,"archived":false,"fork":false,"pushed_at":"2021-01-15T19:47:11.000Z","size":208,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-05T06:43:10.002Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sccn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-01-07T20:48:15.000Z","updated_at":"2023-07-25T14:42:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"86ebc59e-c2ab-462b-863b-fb9167141d2e","html_url":"https://github.com/sccn/std_clust2ch","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_clust2ch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_clust2ch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_clust2ch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_clust2ch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sccn","download_url":"https://codeload.github.com/sccn/std_clust2ch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240347944,"owners_count":19787236,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T18:26:21.051Z","updated_at":"2025-02-23T16:50:52.088Z","avatar_url":"https://github.com/sccn.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"std_clust2ch() is a renewed version of std_selectICsByCluster(). I\nforgot to use std_selectICsByCluster, and when I used it did not work.\nWhen I tried to fix the problem, I found code was not very clear. Thus I\nre-made it into a simpler, more intuitive version.\n\nWhat does std_clust2ch do?\n--------------------------\n\n1.  std_clust2ch performs cluster-level IC selection \u0026 backprojection to\n    generate another set of .set files. All the ICs rejected in the\n    process of creating the final cluster selection will be excluded\n    (i.e., rejected) in the newly generated .set files. The IC rejection\n    mask is saved in EEG.etc.originalIcIdxBeforeClusterIcSelection.\n2.  std_clust2ch performs IC cluster-to-scalp channel projection and\n    computes either percent variance accounted for (PVAF) or area under\n    a curve (AUC) explained by the ICs included by the selected\n    clusters.\n\nGUI screenshots and comments (09/13/2018 updated)\n-------------------------------------------------\n\n![Clust2ch_001.png](images/Clust2ch_001.png)\n\nThis is how GUI menu on EEGLAB STUDY looks. Precompute first, then plot.\n\n![Clust2ch_002.png](images/Clust2ch_002.png)\n\nIf you specify the save path, the plugin will save the new .set files\nafter rejecting ICs. The generated .set files can be used straight for\nSIFT and MPT.\n\n![Fixed001.png](images/Fixed001.png)\n\nFor this plugin, percent variance accounted for (PVAF) measures variance\nacross time within a user-specified window, and percent power accounted\nfor (PPAF) measures IC's contribution to channel in power. In EEGLAB\nenvtopo() functions, PVAF and PPAF are calculated as follows.\n\n`PVAF = 100 - 100 x meanAcrossTime(varAcrossChannels(allICs - selectedICs))/meanAcrossTime(varAcrossChannels(allICs))`\n`PPAF = 100 - 100 x meanAcrossTime((allICs - selectedICs).^2)/meanAcrossTime(allICs.^2)`\n\nNote that calculation of variance removes DC part of the selected\nwindow, which is fine for envtopo() PVAF because zero-sum assumption is\nguaranteed by average referencing. However, DC difference between\ncontributor (IC ERP) and receiver (scalp channel ERP) is critical\ninformation in ERP (i.e., which P300 is larger!) To address this, using\nPPAF may be more optimal.\n\nAs an alternative measure, area under a curve (AUC) is implemented as\nwell. AUC would be suitable for examining a time window around a broad\npeak/trough.\n\nSee also the older version\n--------------------------\n\nSee also [this page](https://sccn.ucsd.edu/wiki/Std_clust2ch) for\nfurther information about the older version called\n*std_selectICsByCluster()*.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fstd_clust2ch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsccn%2Fstd_clust2ch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fstd_clust2ch/lists"}