http://bbci.de/competition/iv/
Dataset 3: ‹hand movement direction in MEG›
provided by the Brain Machine Interfacing Initiative, Albert-Ludwigs-University Freiburg, the Bernstein Center for Computational Neuroscience Freiburg and the Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen (Stephan Waldert, Carsten Mehring, HubertPreissl, Christoph Braun) 

In short: 2 subjects were recorded moving a joystick with their hand and wrist only in one of four directions (right, up, down, left) of their choice after hearing a prompt, using Magnetoencephalography (MEG). The task is to classify the direction of movement from the MEG data. 
The train/test split given corresponds to the exact split provided in the original competition, with the trails for the two subjects merged, however. Data remains split by subject in the folders Subject1 and Subject2. 

From the file 'desc_3.pdf'; a description of the data from the original formatters at the above website

'Title: Directionally modulated MEG activity

Short description:
The data set contains directionally modulated MEG activity that was recorded while
subjects performed wrist movements in four different directions.

Detailed Description:
Brain activity during wrist movements was recorded with MEG at 625 Hz from two
healthy, right-handed subjects. The subject sat relaxed in an MEG chair, the elbow
rested on a pillow to prevent upper arm and shoulder movements, and the head was
stabilized by small pillows. The task was to move a joystick from a center position
toward one of four targets located radially at 90° intervals (four-class center-out
paradigm) using exclusively the right hand and wrist. Movement amplitude was 4.5 cm.
In each trial, the target was self-chosen by the subject. Targets were arranged in the
form of a rhombus in the horizontal plane with corners pointing left, right, away from
and toward the subject's body.
Visual trigger signals were presented on a screen to start a trial or to indicate possible
time violations. A trial started with the joystick in the center position and the appearance
of a gray circle. After a variable delay (1-2 s), the disappearance of the circle indicated
the “go” signal (cued movements). Then, within 0.75 s the subject had to start the
movement and reach the target. For a trial to be valid, the subject also had to rest at the
target for at least 1 s. These time constraints allowed for temporal consistency across
trails and the hold period at the target prevented interference of in- and outward
movements. A red cross was presented continuously for fixation.
The trials were cut to contain data from 0.4 s before to 0.6 s after movement onset and
the signals were band pass filtered (0.5 to 100 Hz) and resampled at 400 Hz, whereas in
Waldert et al. (JNeurosci 28(4), 2008) we showed that especially the low-frequency
activity (<8 Hz) contains information about movement direction. The data are composed
of signals from ten MEG channels which were located above the motor areas. We will
evaluate the competitor's submissions with respect to the percentage of correct
classifications (decoding accuracy). Please note that classification using cross-validation
on the provided training data does not necessarily yield the average decoding accuracy
reported in our original study because of the reduced number of channels (approx. one
quarter), inter-subject variability and the reduced number of trials in the training set due
to separation of data into training and test set. The data are provided as mat-files
(Matlab), detailed description is provided along with the files. Predicted class labels for
the test sets must be submitted as a single mat-file containing two variables (vectors):
PredictedLabelsS1 and PredictedLabelsS2, e.g. PredictedLabelsS1=[3 2 4 1 ...]. The
name of this mat-file must identify the competitor.'

