# Historique: Task1: Seperation of dependent components

## Comparaison de la version 28 à la version 35

## Causal Analysis of simulated EEG data## Description of taskMotivation: Noninvasive electrophysiological measurements like EEG/MEG measure to large extent unknown superpositions of very many sources. Any relation observed between channels is dominated by meaningless mixtures of mainly independent sources. The question is how to observe and properly interpret true interactions in the presence of such strong confounders.Download data here. To read the data into MATLAB, typefid=fopen('simuldata.bin'); 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 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".For all examples either 1 or -1 is correct. The most important point here is the way it is counted: you get +1 point for each correct answer; you get -10 points for each wrong answer; and you get 0 points for each 0 in the result vector. With this counting confidence about the result is added into the evaluation. It is strongly recommended that for each example the evidence for a specific finding is assessed. ## SubmissionThe 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
Remark: The total points can be calculated as the number of correct detections minus ten times the number of false detections. |
## Causal Analysis of simulated EEG data## Description of taskMotivation: Noninvasive electrophysiological measurements like EEG/MEG measure to large extent unknown superpositions of very many sources. Any relation observed between channels is dominated by meaningless mixtures of mainly independent sources. The question is how to observe and properly interpret true interactions in the presence of such strong confounders.Download data here. To read the data into MATLAB, typefid=fopen('simuldata.bin'); 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 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".For all examples either 1 or -1 is correct. The most important point here is the way it is counted: you get +1 point for each correct answer; you get -10 points for each wrong answer; and you get 0 points for each 0 in the result vector. With this counting confidence about the result is added into the evaluation. It is strongly recommended that for each example the evidence for a specific finding is assessed. ## SubmissionThe 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) . ## ResultsTo see the algorithm details, click the submitter's name.
Remark: The total points can be calculated as the number of correct detections minus ten times the number of false detections. |