moabb.datasets.BNCI2015_008#

class moabb.datasets.BNCI2015_008(subjects=None, sessions=None)[source]#

BNCI 2015-008 Center Speller P300 dataset.

Dataset summary

#Subj

13

#Chan

63

#Trials / class

varies NT / T

Trials length

1 s

Freq

250 Hz

#Sessions

2

Participants

  • Population: Healthy

  • Age: 27 (range: 16-45) years

  • Handedness: {‘right’: 12, ‘left’: 1}

  • BCI experience: naive

Equipment

  • Amplifier: Brain Products actiCAP

  • Electrodes: active electrode

  • Montage: 10-10

  • Reference: left mastoid

Preprocessing

  • Data state: filtered

  • Bandpass filter: 0.016-250 Hz

  • Steps: downsampling, lowpass filter, baseline correction

  • Re-reference: linked mastoids

  • Notes: For offline ERP analysis: downsampled to 250 Hz, lowpass filtered below 49 Hz using Chebyshev filter (passbands/stopbands: 42/49 Hz). For online classification: downsampled to 100 Hz, no software filter applied. Baseline correction using -200 ms prestimulus interval.

Data Access

  • DOI: 10.1088/1741-2560/8/6/066003

  • Data URL: bbci/bbci_public

  • Repository: GitHub

Experimental Protocol

  • Paradigm: p300

  • Feedback: none

  • Stimulus: visual_flash

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Dataset summary

Name

#Subj

#Chan

#Trials/class

Trials length

Sampling Rate

#Sessions

BNCI2015_008

13

63

~1180 T / ~5900 NT

1.0s

250Hz

2

Dataset from [1], also known as Treder2011.

Dataset Description

This dataset contains P300 evoked potentials recorded during a gaze-independent two-stage visual speller paradigm called the “Center Speller”. Unlike traditional matrix spellers that require gaze fixation on target cells, the Center Speller allows users to focus on the screen center while covertly attending to peripheral stimuli.

The paradigm uses a two-stage selection process where users first select a group of characters, then select individual characters within that group. This design enables efficient spelling without requiring eye movements, making it suitable for users with severe motor disabilities affecting eye control.

Participants

  • 13 healthy subjects

  • BCI experience: Previous experience with P300-based BCIs

  • Location: Machine Learning Laboratory, TU Berlin, Germany

Recording Details

  • Channels: 63 EEG electrodes (standard 10-10 system)

  • Sampling rate: 250 Hz

  • Reference: Nose reference

Data Organization

Event Codes

  • Target (1): Target stimulus presented (attended)

  • NonTarget (2): Non-target stimulus presented (not attended)

References

[1]

Treder, M. S., Schmidt, N. M., & Blankertz, B. (2011). Gaze-independent brain-computer interfaces based on covert attention and feature attention. Journal of Neural Engineering, 8(6), 066003. https://doi.org/10.1088/1741-2560/8/6/066003

Notes

Added in version 1.2.0.

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