moabb.datasets.Zhou2016#
- class moabb.datasets.Zhou2016[source]#
Motor Imagery dataset from Zhou et al 2016.
PapersWithCode leaderboard: https://paperswithcode.com/dataset/zhou2016-moabb
Dataset summary
#Subj
4
#Chan
14
#Classes
3
#Trials / class
160
Trials length
5 s
Freq
250 Hz
#Sessions
3
#Runs
2
Total_trials
11496
Participants
Population: healthy
BCI experience: prior experience in the experimental paradigm
Equipment
Amplifier: BCI2000
Montage: 10-20
Reference: Car
Preprocessing
Data state: raw EEG available
Bandpass filter: 0.1-100 Hz
Steps: bandpass filtering
Re-reference: car
Data Access
DOI: 10.1371/journal.pone.0162657
Experimental Protocol
Paradigm: imagery
Feedback: visual cue (red arrow)
Stimulus: avatar
Dataset from the article A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface [1]. This dataset contains data recorded on 4 subjects performing 3 type of motor imagery: left hand, right hand and feet.
Every subject went through three sessions, each of which contained two consecutive runs with several minutes inter-run breaks, and each run comprised 75 trials (25 trials per class). The intervals between two sessions varied from several days to several months.
A trial started by a short beep indicating 1 s preparation time, and followed by a red arrow pointing randomly to three directions (left, right, or bottom) lasting for 5 s and then presented a black screen for 4 s. The subject was instructed to immediately perform the imagination tasks of the left hand, right hand or foot movement respectively according to the cue direction, and try to relax during the black screen.
References
[1]Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016) A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9). https://doi.org/10.1371/journal.pone.0162657
