{"action":"created","issue":{"url":"https://api.github.com/repos/mne-tools/mne-python/issues/2112","labels_url":"https://api.github.com/repos/mne-tools/mne-python/issues/2112/labels{/name}","comments_url":"https://api.github.com/repos/mne-tools/mne-python/issues/2112/comments","events_url":"https://api.github.com/repos/mne-tools/mne-python/issues/2112/events","html_url":"https://github.com/mne-tools/mne-python/issues/2112","id":77706028,"number":2112,"title":"ENH: Denoising options for preprocessing","user":{"login":"teonlamont","id":1578674,"avatar_url":"https://avatars.githubusercontent.com/u/1578674?v=3","gravatar_id":"","url":"https://api.github.com/users/teonlamont","html_url":"https://github.com/teonlamont","followers_url":"https://api.github.com/users/teonlamont/followers","following_url":"https://api.github.com/users/teonlamont/following{/other_user}","gists_url":"https://api.github.com/users/teonlamont/gists{/gist_id}","starred_url":"https://api.github.com/users/teonlamont/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/teonlamont/subscriptions","organizations_url":"https://api.github.com/users/teonlamont/orgs","repos_url":"https://api.github.com/users/teonlamont/repos","events_url":"https://api.github.com/users/teonlamont/events{/privacy}","received_events_url":"https://api.github.com/users/teonlamont/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":{"url":"https://api.github.com/repos/mne-tools/mne-python/milestones/8","html_url":"https://github.com/mne-tools/mne-python/milestones/0.12","labels_url":"https://api.github.com/repos/mne-tools/mne-python/milestones/8/labels","id":1429321,"number":8,"title":"0.12","description":"","creator":{"login":"Eric89GXL","id":2365790,"avatar_url":"https://avatars.githubusercontent.com/u/2365790?v=3","gravatar_id":"","url":"https://api.github.com/users/Eric89GXL","html_url":"https://github.com/Eric89GXL","followers_url":"https://api.github.com/users/Eric89GXL/followers","following_url":"https://api.github.com/users/Eric89GXL/following{/other_user}","gists_url":"https://api.github.com/users/Eric89GXL/gists{/gist_id}","starred_url":"https://api.github.com/users/Eric89GXL/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Eric89GXL/subscriptions","organizations_url":"https://api.github.com/users/Eric89GXL/orgs","repos_url":"https://api.github.com/users/Eric89GXL/repos","events_url":"https://api.github.com/users/Eric89GXL/events{/privacy}","received_events_url":"https://api.github.com/users/Eric89GXL/received_events","type":"User","site_admin":false},"open_issues":15,"closed_issues":1,"state":"open","created_at":"2015-11-25T13:36:18Z","updated_at":"2015-11-26T11:50:42Z","due_on":"2016-04-29T04:00:00Z","closed_at":null},"comments":7,"created_at":"2015-05-18T15:46:14Z","updated_at":"2015-11-29T16:38:51Z","closed_at":null,"body":"It would be nice if we had some denoising options available for preprocessing data. A few options that I was thinking of tsPCA (Cheveigné, 2007), and CALM (Adachi, 2001), which can be a special case of tsPCA.\r\n\r\nthere's a package on git (https://github.com/pealco/python-meg-denoise) that contains an implementation, as well as source code (http://www.isr.umd.edu/Labs/CSSL/simonlab/Denoising.html).\r\n\r\nuse case for those whose lab implement these tools in Matlab, or private software, those don't have license for maxfilter but would like cleaner data."},"comment":{"url":"https://api.github.com/repos/mne-tools/mne-python/issues/comments/160431081","html_url":"https://github.com/mne-tools/mne-python/issues/2112#issuecomment-160431081","issue_url":"https://api.github.com/repos/mne-tools/mne-python/issues/2112","id":160431081,"user":{"login":"kingjr","id":4881164,"avatar_url":"https://avatars.githubusercontent.com/u/4881164?v=3","gravatar_id":"","url":"https://api.github.com/users/kingjr","html_url":"https://github.com/kingjr","followers_url":"https://api.github.com/users/kingjr/followers","following_url":"https://api.github.com/users/kingjr/following{/other_user}","gists_url":"https://api.github.com/users/kingjr/gists{/gist_id}","starred_url":"https://api.github.com/users/kingjr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kingjr/subscriptions","organizations_url":"https://api.github.com/users/kingjr/orgs","repos_url":"https://api.github.com/users/kingjr/repos","events_url":"https://api.github.com/users/kingjr/events{/privacy}","received_events_url":"https://api.github.com/users/kingjr/received_events","type":"User","site_admin":false},"created_at":"2015-11-29T16:38:50Z","updated_at":"2015-11-29T16:38:50Z","body":"Thanks. In our KIT, there are 157 whole-head axial gradiometers and 3 (xyz)\nreference magnetometers.\n\nOn 29 November 2015 at 00:23, Samu Taulu <notifications@github.com> wrote:\n\n> It makes sense to apply SSS on the axial gradiometers after leaving out\n> the reference sensors. However, if the reference sensor signals are\n> available in the data without the coupling to the axial gradiometers, it\n> would be even better to use all channels in SSS (axial gradiometers +\n> refs). By coupling I mean extrapolation and subtraction of the\n> reference-based interference, which is the way the reference channels are\n> traditionally used. Just to double check: How many axial gradiometers are\n> there in the KIT system?\n>\n> —\n> Reply to this email directly or view it on GitHub\n> <https://github.com/mne-tools/mne-python/issues/2112#issuecomment-160372765>\n> .\n>\n"}}