Data set IVa
‹motor imagery, small training sets›
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
⟨benjamin.blankertz@tu-berlin.de⟩
The Thrill
When taking a machine learning approach to Brain-Computer Interfacing,
one has to have labelled training data to teach the classifer. To this
end, the user usually performs a boring calibration measurement before
starting with BCI feedback applications. One important objective in
BCI research is to reduce the time needed for the initial measurement.
This data set poses the challenge of getting along with only a little
amount of training data. One approach to the problem is to use
information from other subjects' measurements to reduce the amount of
training data needed for a new subject. Of course, competitors may
also try algorithms that work on small training sets without using the
information from other subjects.
Experimental Setup
This data set was recorded from five healthy subjects. Subjects sat in
a comfortable chair with arms resting on armrests. This data set contains
only data from the 4 initial sessions without feedback. Visual cues
indicated for 3.5 s which of the following 3 motor imageries
the subject should perform: (L) left hand, (R) right hand,
(F) right foot.
The presentation of target cues were intermitted by periods of random length,
1.75 to 2.25 s, in which the subject could relax.
There were two types of visual stimulation: (1) where targets were
indicated by letters appearing behind a fixation cross
(which might nevertheless induce little target-correlated eye movements),
and (2) where a randomly moving object indicated targets
(inducing target-uncorrelated eye movements). From subjects al and
aw 2 sessions of both types were recorded, while from the
other subjects 3 sessions of type (2) and 1 session of type (1)
were recorded.
Format of the Data
Given are continuous signals of 118 EEG channels and markers that
indicate the time points of 280 cues for each of the 5 subjects
(aa, al, av, aw, ay). For some
markers no target class information is provided (value NaN)
for competition purpose. Only cues for the classes 'right' and 'foot'
are provided for the competition. The following table shows the
respective number of training (labelled) trials "#tr" and test
(unlabelled) trials "#te" for each subject.
|
#tr |
#te |
| aa |
168 |
112 |
| al |
224 |
56 |
| av |
84 |
196 |
| aw |
56 |
224 |
| ay |
28 |
252 |
Data are provided in Matlab format (*.mat) containing
variables:
- 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
- pos: vector of positions of the cue in the EEG signals given in
unit sample, length #cues
- y: vector of target classes (1, 2, or NaN),
length #cues
- className: cell array of class names.
info: structure providing additional information with fields
- 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.
As alternative, data is also provided in zipped ASC II format
(splitted into three files for each subject):
- *_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= right, 2=foot, or 0 for test
trials).
- *_nfo.txt: contains other information as described for the
matlab format.
Requirements and Evaluation
Please provide for each subject an ASC II file (named 'result_IVa_aa.txt',
'result_IVa_al.txt', ...) containing 280 lines of your
estimated class labels (1 or 2) for every cue. (For training trials
this should be the respective value of mrk.y, and for test
trials the output of your algorithm.)
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 performance measure is the overall classification accuracy (number of
correct classified test trials divided by the total number of test trials).
Technical Information
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.
References
- 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.
Note that the above reference describes an older experimental setup.
A new paper analyzing the data sets as provided in this competition
and presenting the feedback results will appear soon.
[ BCI Competition III ]