moabb.datasets.BNCI2015_004#

class moabb.datasets.BNCI2015_004(subjects=None, sessions=None)[source]#

BNCI 2015-004 Mental tasks dataset.

PapersWithCode leaderboard: https://paperswithcode.com/dataset/bnci2015-004-moabb-1

Dataset summary

#Subj

9

#Chan

30

#Classes

5

#Trials / class

80

Trials length

7 s

Freq

256 Hz

#Sessions

2

#Runs

1

Total_trials

7200

Participants

  • Population: CNS tissue damage

  • Clinical population: stroke and spinal cord injury

  • Age: 38 (range: 20-57) years

  • Handedness: not specified

  • BCI experience: naive

Equipment

  • Amplifier: g.tec

  • Electrodes: active electrode

  • Montage: 10-20

  • Reference: left and right mastoid

Preprocessing

  • Data state: filtered

  • Bandpass filter: 0.5-100 Hz

  • Steps: bandpass filter, notch filter, artifact rejection

  • Re-reference: left and right mastoid

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Tasks: word_association, mental_subtraction, spatial_navigation, right_hand_imagery, feet_imagery

  • Feedback: none

  • Stimulus: visual cue

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Dataset from [1].

Dataset Description

This dataset contains EEG data from 9 subjects performing five different mental tasks: mental multiplication, mental letter composing, mental rotation, mental counting, and a baseline task.

References

[1]

Zhang, X., Yao, L., Zhang, Q., Kanhere, S., Sheng, M., & Liu, Y. (2017). A survey on deep learning based brain computer interface: Recent advances and new frontiers. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 145-163.

Notes

Note

BNCI2015_004 was previously named BNCI2015004. BNCI2015004 will be removed in version 1.1.

Added in version 0.4.0.

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