# History: Task1: Seperation of dependent components

## Comparing version 25 with version 35

@@ -Lines: 11-15 changed to +Lines: 11-15 @@
data=reshape(fread(fid,'float'),6000,2,1000);
- __The data__ consists of 1000 examples of bivariate data for 6000 time points. Each example is a superposition of a signal (of interest) and noise. The signal is constructed from a unidirectional bivariate AR-model of order 10 with (otherwise) random AR-parameters and uniformly distributed input. The noise is constructed of three independent sources, generated with 3 univariate AR-models with random parameters and uniformly distributed input, which were instantaneously mixed into the two sensors with a random mixing matrix. The relative strength of noise and signal was set randomly. The data were generated with this [https://sisec2011.wiki.irisa.fr/tiki-download_wiki_attachment.php?attId=4&page=Task1%3A%20Seperation%20of%20dependent%20components|Matlab code]. Note, that the phrase 'simulated EEG data' is meant loosely. The simulation addresses the conceptual problems of EEG data, but the actual spectra can be quite different from real EEG data.
+ __The data__ consists of 1000 examples of bivariate data for 6000 time points. Each example is a superposition of a signal (of interest) and noise. The signal is constructed from a unidirectional bivariate AR-model of order 10 with (otherwise) random AR-parameters and uniformly distributed input. The noise is constructed of three independent sources, generated with 3 univariate AR-models with random parameters and uniformly distributed input, which were instantaneously mixed into the two sensors with a random mixing matrix. The relative strength of noise and signal was set randomly. The data were generated with this [https://sisec2011.wiki.irisa.fr/tiki-download_wiki_attachment.php?attId=4&page=Task1%3A%20Seperation%20of%20dependent%20components|Matlab code]. Note, that the phrase 'simulated EEG data' is meant loosely. The simulation addresses the conceptual problems of EEG data, but e.g. the actual spectra can be quite different from real EEG data.
__The task__ is to estimate the direction of the interaction of the signal. A submitted result is a vector with 1000 numbers having the values 1, -1, or 0. Here, 1 means direction is from first to second sensor, -1 means direction is from second to first sensor, and 0 means "I don't know". @@ -Lines: 20-27 changed to +Lines: 20-46 @@
!!Submission
- Please send submissions by October 1, 2011 to
+ The deadline for submission of results was October 31, 2011 to be sent to
Guido Nolte email: guido.nolte(at)first.fraunhofer.de
+
+ + In addition, each participant was asked to provide basic information about his/her algorithm (e.g. a bibliographical reference) . + + + !!Results + + To see the algorithm details, click the submitter's name. + + ||Name | City | Total points | Correct detections | False detections |Details + A.M. Bianchi | Milano/Italy | -2289 | 701 | 299 | + ((S. Hu)) | Hangzhou/China | 252 | 352 | 10 | Click name + ((L. Leistritz)) | Jena/Germany | -357 | 773 | 113 | Click name + ((V. Vakorin)) | Toronto/Canada | 218 | 278 | 6 | Click name + ((M. Wibral)) | Frankfurt/Germany | -247 | 163 |41 |[http://sisec2011.wiki.irisa.fr/tiki-download_wiki_attachment.php?attId=7&page=Task1%3A%20Seperation%20of%20dependent%20components|(pdf)]|| + + Remark: The total points can be calculated as the number of correct detections minus ten times + the number of false detections. + |