This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four different motor imagery tasks, namely the imagination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on different days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session. At the beginning of each session, a recording of approximately 5 minutes was performed to estimate the EOG influence. The recording was divided into 3 blocks: (1) two minutes with eyes open (looking at a fixation cross on the screen), (2) one minute with eyes closed, and (3) one minute with eye movements. The timing scheme of one session is illustrated in Figure 1. Note that due to technical problems the EOG block is shorter for subject A04T and contains only the eye movement condition (see Table 1 for a list of all subjects). The subjects were sitting in a comfortable armchair in front of a computer screen. At the beginning of a trial (t = 0 s), a fixation cross appeared on the black screen. In addition, a short acoustic warning tone was presented. After two seconds (t = 2 s), a cue in the form of an arrow pointing either to the left, right, down or up (corresponding to one of the four classes
[1]
G. Pfurtscheller,et al.
A fully automated correction method of EOG artifacts in EEG recordings
,
2007,
Clinical Neurophysiology.
[2]
R. Ward,et al.
EMG and EOG artifacts in brain computer interface systems: A survey
,
2007,
Clinical Neurophysiology.
[3]
G. Pfurtscheller,et al.
Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment
,
2007,
IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[4]
Clemens Brunner,et al.
Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis
,
2007,
Pattern Recognit. Lett..
[5]
G Pfurtscheller,et al.
Seperability of four-class motor imagery data using independent components analysis
,
2006,
Journal of neural engineering.
[6]
José del R. Millán,et al.
Evaluation Criteria for BCI Research
,
2007
.