Dear Akshay Raj Gollahalli 

Goals of the organizers

The goal of the "BCI Competition III" is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs). Compared to the past BCI Competitions, new challanging problems are addressed that are highly relevant for practical BCI systems, such as

session-to-session transfer (data set I)

small training sets, maybe to be solved by subject-to-subject transfer (data set IVa),

non-stationarity problems (data set IIIb, data set IVc),

multi-class problems (data set IIIa, data set V, data set II,),

classification of continuous EEG without trial structure (data set IVb, data set V).

Also this BCI Competition includes for the first time ECoG data (data set I) and one data set for which preprocessed features are provided (data set V) for competitors that like to focus on the classification task rather than to dive into the depth of EEG analysis.

The organizers are aware of the fact that by such a competition it is impossible to validate BCI systems as a whole. But nevertheless we envision interesting contributions to ultimately improve the full BCI.

Goals for the participants

For each data set specific goals are given in the respective description. Technically speaking, each data set consists of single-trials of spontaneous brain activity, one part labeled (training data) and another part unlabeled (test data), and a performance measure. The goal is to infer labels (or their probabilities) for the test set from training data that maximize the performance measure for the true (but to the competitors unknown) test labels. Results will be announced at the Third International BCI Meeting in Rensselaerville, June 14-19, and on this web site. For each data set, the competition winner gets a chance to publish the algorithm in an article devoted to the competition that will appear in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3272647/

http://www.bbci.de/competition/iii/results/

Dear Akshay Raj Gollahalli 

Goals of the organizers

The goal of the "BCI Competition III" is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs). Compared to the past BCI Competitions, new challanging problems are addressed that are highly relevant for practical BCI systems, such as

session-to-session transfer (data set I)

small training sets, maybe to be solved by subject-to-subject transfer (data set IVa),

non-stationarity problems (data set IIIb, data set IVc),

multi-class problems (data set IIIa, data set V, data set II,),

classification of continuous EEG without trial structure (data set IVb, data set V).

Also this BCI Competition includes for the first time ECoG data (data set I) and one data set for which preprocessed features are provided (data set V) for competitors that like to focus on the classification task rather than to dive into the depth of EEG analysis.

The organizers are aware of the fact that by such a competition it is impossible to validate BCI systems as a whole. But nevertheless we envision interesting contributions to ultimately improve the full BCI.

Goals for the participants

For each data set specific goals are given in the respective description. Technically speaking, each data set consists of single-trials of spontaneous brain activity, one part labeled (training data) and another part unlabeled (test data), and a performance measure. The goal is to infer labels (or their probabilities) for the test set from training data that maximize the performance measure for the true (but to the competitors unknown) test labels. Results will be announced at the Third International BCI Meeting in Rensselaerville, June 14-19, and on this web site. For each data set, the competition winner gets a chance to publish the algorithm in an article devoted to the competition that will appear in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3272647/

http://www.bbci.de/competition/iii/results/

Hi Nirmala,

I am well aware of those and thanks for pointing it out. I am trying to get a good accuracy result for my model. Nevertheless, I have to compare it to the actual labels they have. So for that reason, I would need the original labels.

Thanks for the help.

Ok