# 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**: soa_ms=175, stimulus_duration_ms=40, stimulus_intensity_db=58, speaker_arrangement=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**: calibration_per_direction=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**: {'low_cutoff_hz': 0.1, 'high_cutoff_hz': 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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