BNCI 2015-003 P300 dataset

BNCI 2015-003 P300 dataset.

Dataset Overview

• Code: BNCI2015-003
• Paradigm: p300
• DOI: 10.1016/j.neulet.2009.06.045
• Subjects: 10
• Sessions per subject: 1
• Events: Target=2, NonTarget=1
• Trial interval: [0, 0.8] s
• Runs per session: 2
• Session IDs: Session 1, Session 2
• File format: gdf
• Data preprocessed: True
• Number of contributing labs: 1

Acquisition

• Sampling rate: 256.0 Hz
• Number of channels: 8
• Channel types: eeg=8
• Channel names: Fz, Cz, P3, Pz, P4, PO7, Oz, PO8
• Montage: standard_1005
• Hardware: BrainAmp
• Software: Matlab
• Reference: nose
• Sensor type: Ag/AgCl electrodes
• Line frequency: 50.0 Hz
• Online filters: hardware analog band-pass filter between 0.1 and 250 Hz
• Impedance threshold: 15.0 kOhm
• Cap manufacturer: Brain Products
• Electrode type: Ag/AgCl
• Electrode material: silver/silver chloride
• Auxiliary channels: EOG (2 ch, bipolar)

Participants

• Number of subjects: 10
• Health status: patients
• Clinical population: Healthy
• Age: mean=34.1, std=11.4, min=20, max=57
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: p300
• Task type: auditory_oddball
• Number of classes: 2
• Class labels: Target, NonTarget
• Tasks: spelling, auditory_attention
• Study design: Auditory Multi-class Spatial ERP (AMUSE) paradigm using spatial auditory cues from six speaker locations in azimuth plane. Two-step hex-o-spell like interface for character selection. Subjects mentally count target stimuli from one of six spatial directions.
• Study domain: communication
• Feedback type: auditory
• Stimulus type: spatial_auditory
• Stimulus modalities: auditory
• Primary modality: auditory
• Synchronicity: synchronous
• Mode: online
• Training/test split: True
• Instructions: Focus attention to one target direction and mentally count the number of appearances
• Stimulus presentation: soams=175, stimulusdurationms=40, stimulusintensitydb=58, speakerarrangement=6 speakers at ear height, evenly distributed in circle with 60° distance, radius 65 cm

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

  Target
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Target

  NonTarget
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Non-target

Paradigm-Specific Parameters

• Detected paradigm: p300
• Number of targets: 6
• Stimulus onset asynchrony: 175.0 ms

Data Structure

• Trials: 48
• Trials per class: calibrationperdirection=8
• Trials context: calibration_phase

Preprocessing

• Data state: filtered
• Preprocessing applied: True
• Steps: low-pass filter, downsampling, baselining
• Highpass filter: 0.1 Hz
• Lowpass filter: 40.0 Hz
• Bandpass filter: {'lowcutoffhz': 0.1, 'highcutoffhz': 40.0}
• Filter type: analog hardware filter for acquisition; low-pass for online
• Artifact methods: variance criterium, peak-to-peak difference criterium
• Re-reference: nose
• Downsampled to: 100.0 Hz
• Epoch window: [-0.15, None]
• Notes: For online use signal was low-pass filtered below 40 Hz and downsampled to 100 Hz. Data baselined using 150 ms pre-stimulus data as reference.

Signal Processing

• Classifiers: LDA, linear binary classifier
• Feature extraction: spatio-temporal features, r2 coefficient, interval averaging
• Spatial filters: shrinkage regularization (Ledoit-Wolf)

Cross-Validation

• Method: online
• Evaluation type: online

Performance (Original Study)

• Accuracy: 77.4%
• Itr: 2.84 bits/min
• Char Per Min Session1: 0.59
• Char Per Min Session2 Max: 1.41
• Char Per Min Session2 Avg: 0.94
• Itr Session2 Avg: 5.26
• Itr Session2 Max: 7.55
• Success Rate Session1: 76.0

BCI Application

• Applications: speller, communication
• Environment: laboratory
• Online feedback: True

Tags

• Pathology: Healthy
• Modality: Auditory
• Type: ERP, P300

Documentation

• Description: Auditory BCI speller using spatial cues (AMUSE paradigm) allowing purely auditory communication interface
• DOI: 10.1016/j.neulet.2009.06.045
• Associated paper DOI: 10.3389/fnins.2011.00112
• License: CC-BY-NC-ND-4.0
• Investigators: Martijn Schreuder, Thomas Rost, Michael Tangermann
• Senior author: Michael Tangermann
• Contact: schreuder@tu-berlin.de
• Institution: Berlin Institute of Technology
• Department: Machine Learning Laboratory
• Address: Machine Learning Laboratory, Berlin Institute of Technology, FR6-9, Franklinstraße 28/29, 10587 Berlin, Germany
• Country: Germany
• Repository: BNCI Horizon
• Publication year: 2011
• Funding: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG MU 987/3-2); Bundesministerium fur Bildung und Forschung (BMBF FKZ 01IB001A, 01GQ0850); FP7-ICT PASCAL2 Network of Excellence ICT-216886
• Ethics approval: Ethics Committee of the Charité University Hospital
• Acknowledgements: Thomas Denck, David List and Larissa Queda for help with experiments. Klaus-Robert Müller and Benjamin Blankertz for fruitful discussions.
• Keywords: brain-computer interface, directional hearing, auditory event-related potentials, P300, N200, dynamic subtrials

External Links

• Source: http://www.frontiersin.org/neuroprosthetics/10.3389/fnins.2011.00112/abstract

Abstract

This online study introduces an auditory spelling interface that eliminates the necessity for visual representation. In up to two sessions, a group of healthy subjects (N=21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multi-class Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 char/min (7.55 bits/min) could be reached during the second session (average: 0.94 char/min, 5.26 bits/min).

Methodology

Participants surrounded by six speakers at ear height in circle (60° spacing, 65 cm radius). Each direction associated with unique combination of tone (base frequency + harmonics) and band-pass filtered noise. Two-step hex-o-spell interface for character selection. Session 1: calibration (48 trials, 8 per direction, 15 iterations each) followed by online spelling with 15 fixed iterations. Session 2: calibration followed by online spelling with dynamic stopping method (4-15 iterations). Spatio-temporal feature extraction using r2 coefficient and interval selection (2-4 intervals for early and late components, 112-224 features total). Linear binary classifier with shrinkage regularization (Ledoit-Wolf). Decision making based on median classifier scores across iterations.

References

Schreuder, M., Rost, T., & Tangermann, M. (2011). Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Frontiers in neuroscience, 5, 112. https://doi.org/10.3389/fnins.2011.00112

Notes

.. note::

`BNCI2015_003 was previously named BNCI2015003. BNCI2015003` will be removed in version 1.1.

.. versionadded:: 0.4.0 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

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