Data set IVb
‹motor imagery, uncued classifier application›
Data set provided by 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)
Correspondence to Benjamin Blankertz
Most demonstrations of algorithms on BCI data are just evaluating
classification of EEG trials, i.e., windowed EEG signals for fixed
length, where each trial corresponds to a specific mental state.
But in BCI applications with continuous feedback one is faced with
the problem that the classifier has to be applied continuously to
the incoming EEG without having cues of when the subject is
switching her/his intention. This data set poses the challenge of
applying a classifier to continuous EEG for which no cue information
Another issue that is addressed in this data set is that the test data
contains periods in which the user has no control intention. During
those intervals the classifier is supposed to return 0 (no affiliation
to one of the target classes). Our experience is that is does not
help to include a relax class in the training measurement,
because the mental state of relax during an initial recording
(without feedback) is substatially different from the mental state of
having no control intention during feedback. During feedback the user
is in a rather active state, and despite of having no actual control
intention the user may very well have strong considerations concerning
her/his feedback application.
When one can design a classifier that positively recognizes the two
learned classes (here left and foot motor imagery) it
should return zero for other mental states as relax or periods
of absense of control intention.
This data set was recorded from one healthy subject. He sat in a
comfortable chair with arms resting on armrests. This data set
contains only data from the 7 initial sessions without feedback. The
first 3 sessions are given with labels as training set. Visual cues
(letter presentation) indicated for 3.5 seconds which of the following
3 motor imageries the subject should perform: (L) left hand,
(F) right foot, (Z) tongue (=Zunge in german). The
presentation of target cues were intermitted by periods of random
length, 1.75 to 2.25 seconds, in which the subject could
relax. Continous EEG signals of sessions 4 to 7 are given without any
cue information (neither target class nor timing) as test set. In
these sessions the target classes left, foot and
relax were ordered by acoustic stimuli for periods for varying
length between 1.5 and 8 seconds. Intermitting periods were given as
Format of the Data
Given are continuous signals of 118 EEG channels and, for the
training data, markers that indicate the time points of 210 cues and
the corresponding target classes. Only cues for the classes
left and foot are provided for the competition (since
tongue imagery was not performed in the test sessions).
Data are provided in Matlab format (*.mat) containing
info: structure providing additional information with fields
- cnt: the continuous EEG signals, size [time x channels].
The array is stored in datatype INT16. To convert it to
uV values, use cnt= 0.1*double(cnt); in Matlab.
- mrk: structure of target cue information with fields
(the file of test data contains only the first field)
- pos: vector of positions of the cue in the EEG signals given in
unit sample, length #cues
- y: vector of target classes (-1 for left or 1 for
foot), length #cues
As alternative, data is also provided in zipped ASC II format:
- name: name of the data set,
- fs: sampling rate,
- clab: cell array of channel labels,
- xpos: x-position of electrodes in a 2d-projection,
- ypos: y-position of electrodes in a 2d-projection.
- *_cnt.txt: the continuous EEG signals, where each
row holds the values for all channels at a specific time point
- *_mrk.txt: target cue information, each row represents one cue
where the first value defines the time point (given in unit sample),
and the second value the target class (-1 for left or 1 for
foot). The file of test data only contains time points.
- *_nfo.txt: contains other information as described for the
Requirements and Evaluation
Please provide an ASC II file (named 'result_IVb.txt') containing
classifier outputs (real number between -1 and 1) for each sample
point of the test signals, one value per line. (I.e., the file should
have 76566 lines.) The submissions are evaluated in view of a one
dimensional cursor control application with range from -1 to 1. The
mental state left is used to position the cursor at -1, and the
mental state foot is used to position the cursor near 1. In the
absense of those mental states (left and foot) the
cursor should be at position 0. The latter requirement is checked at
intervals in which the subject was instructed to relax. The
output during intermitting periods (in which the subject is not in a
defined mental state) are ignored. Note that it is unknown to the
competitors at what intervals the subject is in a defined mental
state. Competitiors submit classifier outputs for all time points.
The evaluation function calculates the squared error with respect to
the target vector that is -1 for class left, 1 for foot,
and 0 for relax, averaged across all time points for which the
subject is in a defined mental state (these 'active areas' are delayed
for 500ms compared to the stimuli to account for the reaction
You also have to provide a description of the used algorithm (ASC II,
HTML or PDF format) for publication at the results web page.
The recording was made using BrainAmp amplifiers and a 128 channel
Ag/AgCl electrode cap from ECI. 118 EEG channels were measured at
positions of the extended international 10/20-system. Signals were
band-pass filtered between 0.05 and 200 Hz and then digitized at
1000 Hz with 16 bit (0.1 uV) accuracy. We provide also a version
of the data that is downsampled at 100 Hz (by picking each 10th
sample) that we typically use for analysis.
Note that the above reference describes an older experimental setup.
A new paper analyzing data sets similar to the one provided in this
competition and presenting feedback results will appear soon.
- Guido Dornhege, Benjamin
Blankertz, Gabriel Curio, and Klaus-Robert Müller.
Boosting bit rates in non-invasive EEG single-trial classifications by
feature combination and multi-class paradigms.
IEEE Trans. Biomed. Eng., 51(6):993-1002, June 2004.
[ BCI Competition III ]