Validating in algorithm
Wolpaw, Gerwin Schalk, Dean Krusienski) [36 classes, 64 EEG channels (0.1-60Hz), 240Hz sampling rate, 85 training and 100 test trials, recorded with the BCI2000 system] Data sets IIIa: ‹motor imagery, multi-class› (description IIIa) provided by the Laboratory of Brain-Computer Interfaces (BCI-Lab), Graz University of Technology, (Gert Pfurtscheller, Alois Schlögl) [4 classes, 60 EEG channels (1-50Hz), 250Hz sampling rate, 60 trials per class] Data sets IIIb: ‹motor imagery with non-stationarity problem› (description IIIb, additional information) provided by TU-Graz (as above) [2 classes, 2 bipolar EEG channels 0.5-30Hz, 125Hz sampling rate, 60 trials per class] Data set IVa: ‹motor imagery, small training sets› (description IVa) provided by the Berlin BCI group: Fraunhofer FIRST, Intelligent Data Analysis Group (Klaus-Robert Müller, Benjamin Blankertz), and Campus Benjamin Franklin of the Charité - University Medicine Berlin, Department of Neurology, Neurophysics Group (Gabriel Curio) cued motor imagery with 2 classes (right hand, foot) from 5 subjects; from 2 subjects most trials are labelled (resp.
80% and 60%), while from the other 3 less and less training data are given (resp.
[ goals | news | data sets | schedule | submission | download | organizers | references ] 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 Also this BCI Competition includes for the first time ECo G 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.
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.
[ top ] Albany: Gerwin Schalk, Dean Krusienski, Jonathan R.
The most common use for FSUM is checking data files for corruption.
A message digest or checksum calculation might be performed on data before transferring it from one location to another.
Making the same calculation after the transfer and comparing the before and after results, you can determine if the received data is corrupted or not.
If the results match, then the received data is likely accurate.