{"action":"created","issue":{"url":"https://api.github.com/repos/scot-dev/scot/issues/49","labels_url":"https://api.github.com/repos/scot-dev/scot/issues/49/labels{/name}","comments_url":"https://api.github.com/repos/scot-dev/scot/issues/49/comments","events_url":"https://api.github.com/repos/scot-dev/scot/issues/49/events","html_url":"https://github.com/scot-dev/scot/issues/49","id":56844243,"number":49,"title":"Example implementation of SCoT with EEGLAB in MATLAB","user":{"login":"pntfx4","id":6784552,"avatar_url":"https://avatars.githubusercontent.com/u/6784552?v=3","gravatar_id":"","url":"https://api.github.com/users/pntfx4","html_url":"https://github.com/pntfx4","followers_url":"https://api.github.com/users/pntfx4/followers","following_url":"https://api.github.com/users/pntfx4/following{/other_user}","gists_url":"https://api.github.com/users/pntfx4/gists{/gist_id}","starred_url":"https://api.github.com/users/pntfx4/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pntfx4/subscriptions","organizations_url":"https://api.github.com/users/pntfx4/orgs","repos_url":"https://api.github.com/users/pntfx4/repos","events_url":"https://api.github.com/users/pntfx4/events{/privacy}","received_events_url":"https://api.github.com/users/pntfx4/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":13,"created_at":"2015-02-06T17:49:36Z","updated_at":"2015-02-23T08:53:39Z","closed_at":null,"body":"Can you suggest a means of utilizing this toolbox for ICA decomposition by way of EEGLAB in MATLAB.\r\n\r\nCurrently, to run the binary ICA the following code is executed: EEG = pop_runica(EEG,'icatype','binica','dataset',1,'options',{'extended',1},'chanind',1:size(EEG.chanlocs,2));\r\n\r\nMy preference would be to instead utilize SCoT (particularly as it appears possible to specify a non-random seed), having the python functions return the correct information to the EEGLAB structure.\r\n\r\nAn example of such an implementation would be very helpful!"},"comment":{"url":"https://api.github.com/repos/scot-dev/scot/issues/comments/75507446","html_url":"https://github.com/scot-dev/scot/issues/49#issuecomment-75507446","issue_url":"https://api.github.com/repos/scot-dev/scot/issues/49","id":75507446,"user":{"login":"kazemakase","id":2392396,"avatar_url":"https://avatars.githubusercontent.com/u/2392396?v=3","gravatar_id":"","url":"https://api.github.com/users/kazemakase","html_url":"https://github.com/kazemakase","followers_url":"https://api.github.com/users/kazemakase/followers","following_url":"https://api.github.com/users/kazemakase/following{/other_user}","gists_url":"https://api.github.com/users/kazemakase/gists{/gist_id}","starred_url":"https://api.github.com/users/kazemakase/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kazemakase/subscriptions","organizations_url":"https://api.github.com/users/kazemakase/orgs","repos_url":"https://api.github.com/users/kazemakase/repos","events_url":"https://api.github.com/users/kazemakase/events{/privacy}","received_events_url":"https://api.github.com/users/kazemakase/received_events","type":"User","site_admin":false},"created_at":"2015-02-23T08:53:39Z","updated_at":"2015-02-23T08:53:39Z","body":"Yes. MVARICA performs the ICA decomposition on the residuals of a VAR model (of order `model_order`).\r\nTypically the data is pre-transformed with PCA, but this is not necessary if you want to get the maximum number of ICA sources.\r\n\r\nI normally set the model order to a value that I need to use later in my application too. I do not know what happens if you are interested in the ICA decomposition and the model order is too high. Most likely you get bad results because the VAR model overfits and the residuals are too low.\r\n\r\nModel order selection can be automated (this can be computationally demanding and only works if you have multiple trials):\r\n\r\n    var.optimize_order(scotdata)\r\n    r = scotdata - var.predict(scotdata)\r\n    u = infomax(r, extended=True).T"}}