[ goals | news | data sets | schedule | submission | download | organizers | references ]
Data set I:
‹motor imagery in ECoG recordings,
session-to-session transfer›
(description I)
provided by
Eberhard-Karls-Universität
Tübingen, Germany, Dept. of Computer Engineering and
Dept.
of Medical Psychology and Behavioral Neurobiology
(Niels Birbaumer), and
Max-Planck-Institute for
Biological Cybernetics, Tübingen, Germany
(Bernhard Schökopf),
and
Universität Bonn, Germany,
Dept. of Epileptology
cued motor imagery (left pinky, tongue) from one subject; training and
test data are ECoG recordings from two different sessions with about one
week in between
[2 classes, 64 ECoG channels (0.016-300Hz), 1000Hz sampling rate,
278 training and 100 test trials]
Data set II:
‹P300 speller paradigm›
(description II)
provided by
Wadsworth Center, NYS Department of Health
(Jonathan R. Wolpaw, Gerwin Schalk, Dean Krusienski)
the goal is to estimate to which letter of a 6-by-6 matrix with
successively intensified rows resp. columns the subject was paying
attention to; data from 2 subjects
[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)
cued motor imagery with 4 classes (left hand, right hand, foot, tongue)
from 3 subjects (ranging from quite good to fair performance);
performance measure: kappa-coefficient
[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)
cued motor imagery with online feedback (non-stationary classifier)
with 2 classes (left hand, right hand) from 3 subjects;
performance measure: mutual information
[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. 30%, 20% and 10%); the challenge
is to make a good classification even from little training data, thereby
maybe using information from other subjects with many labelled trials.
[2 classes, 118 EEG channels (0.05-200Hz), 1000Hz sampling rate,
280 trials per subject]
Data set IVb:
‹motor imagery, uncued classifier application›
(description IVb)
provided by the Berlin BCI group (see above)
training data is cued motor imagery with 2 classes (left hand, foot)
from 1 subject, while test data is continuous (i.e., non-epoched) EEG;
the challenge is to provide classifier outputs for each time point,
although it is unknown to the competitors at what time points mental
states changed;
performance measure: mutal information with true labels (-1: left hand,
1: foot, 0: rest) averaged over all samples
[2 classes, 118 EEG channels (0.05-200Hz), 1000Hz sampling rate,
210 training trials, 12 minutes of continuous EEG for testing]
Data set IVc:
‹motor imagery, time-invariance problem›
(description IVc)
provided by the Berlin BCI group (see above)
cued motor imagery with 2 classes (left hand, foot) from 1 subject
(training data is the same as for data set IVb); test data was recorded
4 hours after the training data and contain an additional class 'relax';
performance measure: mutal information with true labels (-1: left hand,
1: foot, 0: relax) averaged over all trials
[2 classes, 118 EEG channels (0.05-200Hz), 1000Hz sampling rate,
210 training trials, 420 test trials]
Data set V:
‹mental imagery, multi-class›
(description V)
provided by
IDIAP Research Institute
(José
del R. Millán)
cued mental imagery with 3 classes (left hand, right hand, word association)
from 3 subjects; besides the raw signals also precomputed features are
provided
[3 classes, 32 EEG channels (DC-256Hz), 512Hz sampling rate,
continuous EEG and precomputed features]
December 12th 2004: launching of the competition
May 22nd 2005, midnight CET to May 23rd: deadline for
submissions
June 16th 2005 (approx.): announcement of the results on
this web site
Submissions to a data set are to be sent to the responsible contact
person as stated in the data set description. The submission has to
comprise the estimated labels, names and affiliations of all involved
researchers and a short note on the involved processing techniques. We
send confirmations for each submission we get. If you do not receive
a confirmation within 2 days please resend your email and inform other
organizing committee members, e.g.,
⟨alois.schloegl@tugraz.at⟩,
⟨benjamin.blankertz@tu-berlin.de⟩,
⟨schalk@wadsworth.org⟩,
⟨schroedm@informatik.uni-tuebingen.de⟩,
⟨jose.millan@idiap.ch⟩
One researcher may NOT submit multiple results to one data set. She/he
has to decide for her/his favorite one. However: From one research
group multiple submissions to one data set are possible. The sets of
involved researchers do not have to be disjoint, but (1) the 'first
author' (contributor) should be distinct, and (2) approaches should be
substantially different.
For details on how to submit your results please refer to the
description of the respective data set. If questions remain unanswered
send an email to the responsable contact person for the specific data
set which is indicated in the description.
Submissions are evaluated for each data set separately. There is no
need to submit for all data sets in order to participate.
Each participant agrees to deliver an extended description (1-2 pages)
of the used algorithm for publication until July 31st 2005 in case
she/he is the winner for one of the data sets.
Each participant has to agree to give reference to the group(s) which recorded the data and to cite (one of) the paper listed in the respective description in each of her/his publications where one of those data sets is analyzed. Furthermore, we request each author to report any publication involving BCI Competiton data sets to us for including it in our list.
[ top ]
Albany: Gerwin Schalk, Dean Krusienski, Jonathan R. Wolpaw
Berlin: Benjamin Blankertz, Guido Dornhege, Klaus-Robert Müller
Graz: Alois Schlögl, Bernhard Graimann, Gert Pfurtscheller
Martigny: Silvia Chiappa, José del R. Millán
Tübingen: Michael Schröder, Thilo Hinterberger, Thomas Navin Lal, Guido Widman, Niels Birbaumer
[ top ]