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# BNCI 2015-010 RSVP P300 dataset

BNCI 2015-010 RSVP P300 dataset.

## Dataset Overview

- **Code**: BNCI2015-010
- **Paradigm**: p300
- **DOI**: 10.1016/j.clinph.2012.12.050
- **Subjects**: 12
- **Sessions per subject**: 1
- **Events**: Target=1, NonTarget=2
- **Trial interval**: [0, 0.8] s
- **Runs per session**: 2
- **Session IDs**: calibration, copy-spelling, free-spelling
- **File format**: EEG
- **Data preprocessed**: True

## Acquisition

- **Sampling rate**: 200.0 Hz
- **Number of channels**: 63
- **Channel types**: eeg=63
- **Channel names**: Fp1, Fp2, AF3, AF4, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, PO9, PO10, Oz, O1, O2
- **Montage**: 10-20
- **Hardware**: BrainAmp amplifiers
- **Software**: Python with Pyff framework
- **Reference**: left mastoid
- **Sensor type**: active electrode
- **Line frequency**: 50.0 Hz
- **Online filters**: lowpass Chebyshev filter up to 40 Hz
- **Impedance threshold**: 10.0 kOhm
- **Cap manufacturer**: Brain Products
- **Cap model**: actiCap
- **Electrode type**: active electrode

## Participants

- **Number of subjects**: 12
- **Health status**: patients
- **Clinical population**: Healthy
- **Age**: mean=29.17, std=8.4, min=24, max=55
- **Gender distribution**: male=6, female=6
- **Handedness**: all right-handed
- **BCI experience**: mixed
- **Species**: human

## Experimental Protocol

- **Paradigm**: p300
- **Task type**: spelling
- **Number of classes**: 2
- **Class labels**: Target, NonTarget
- **Trial duration**: 46.5 s
- **Study design**: RSVP (Rapid Serial Visual Presentation) BCI speller where 30 symbols are presented one-by-one in random order at the center of the screen. Three conditions tested: NoColor 116ms SOA, Color 116ms SOA, and Color 83ms SOA. Colors used to facilitate discrimination.
- **Feedback type**: visual
- **Stimulus type**: RSVP letters
- **Stimulus modalities**: visual
- **Primary modality**: visual
- **Synchronicity**: synchronous
- **Mode**: online
- **Training/test split**: True
- **Instructions**: Participants fixate center of screen, concentrate on target letter, silently count its occurrences. Avoid blinking during visual presentation.

## 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**: 30
- **Number of repetitions**: 10
- **Stimulus onset asynchrony**: 116.0 ms

## Data Structure

- **Trials**: 10 sequences of 30 symbols
- **Blocks per session**: 3
- **Trials context**: per sequence

## Preprocessing

- **Data state**: filtered
- **Preprocessing applied**: True
- **Steps**: lowpass filter, downsampling, baseline correction, artifact rejection
- **Lowpass filter**: 40.0 Hz
- **Filter type**: Chebyshev
- **Filter order**: passband up to 40 Hz, stopband starting at 49 Hz
- **Artifact methods**: min-max criterion for eye movement rejection (75 µV on F9, Fz, F10, AF3, AF4), broadband power rejection (5-40 Hz)
- **Re-reference**: linked mastoids (offline)
- **Downsampled to**: 200.0 Hz
- **Epoch window**: [-0.1, 1.2]
- **Notes**: Baseline correction on pre-stimulus interval (116ms for 116ms SOA, 83/2ms for 83ms SOA). Non-target epochs excluded if 3 preceding or following symbols were targets.

## Signal Processing

- **Classifiers**: LDA with shrinkage
- **Feature extraction**: spatio-temporal features, averaged voltages within time windows
- **Frequency bands**: alpha=[7, 13] Hz
- **Spatial filters**: 55 channels used for classification (all except Fp1,2, AF3,4, F9,10, FT7,8)

## Cross-Validation

- **Method**: calibration/test split
- **Evaluation type**: within_session

## Performance (Original Study)

- **Accuracy**: 94.8%
- **Mean Spelling Rate Symb Per Min**: 1.43
- **Trial Duration 116Ms Soa S**: 46.5
- **Trial Duration 83Ms Soa S**: 36.6

## BCI Application

- **Applications**: speller, communication
- **Environment**: laboratory
- **Online feedback**: True

## Tags

- **Pathology**: Healthy
- **Modality**: Visual
- **Type**: ERP

## Documentation

- **DOI**: 10.1016/j.clinph.2012.12.050
- **License**: CC-BY-NC-ND-4.0
- **Investigators**: Laura Acqualagna, Benjamin Blankertz
- **Senior author**: Benjamin Blankertz
- **Contact**: laura.acqualagna@tu-berlin.de; benjamin.blankertz@tu-berlin.de
- **Institution**: Berlin Institute of Technology
- **Department**: Machine Learning Laboratory; Neurotechnology Group
- **Country**: Germany
- **Repository**: BNCI Horizon
- **Publication year**: 2013
- **Funding**: BMBF Grant; Grant Nos s; Grant No. MU MU; DFG Grant
- **Ethics approval**: Study performed in accordance with the declaration of Helsinki
- **Keywords**: Brain Computer Interfaces, RSVP, ERPs, Speller, P300, N2, gaze-independent

## Abstract

A Brain Computer Interface (BCI) speller using rapid serial visual presentation (RSVP) paradigm for gaze-independent mental typewriting. Twelve healthy participants successfully operated the RSVP speller with mean online spelling rate of 1.43 symb/min and mean symbol selection accuracy of 94.8%. The RSVP speller does not require gaze shifts and can be operated by non-spatial visual attention, making it suitable for patients with impaired oculo-motor control.

## Methodology

Three experimental conditions tested (NoColor 116ms, Color 116ms, Color 83ms SOA). Each condition included calibration, copy-spelling, and free-spelling phases. Vocabulary of 30 symbols presented one-by-one at screen center in pseudo-random order. EEG recorded at 1000 Hz with 63 channels, downsampled to 200 Hz for ERP analysis. Classification using LDA with shrinkage on spatio-temporal features from 5 individually selected time windows. Symbol selection based on averaged classifier output across 10 sequences.

## References

Acqualagna, L., & Blankertz, B. (2013). Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clinical Neurophysiology, 124(5), 901-908. https://doi.org/10.1016/j.clinph.2012.12.050

Notes

.. versionadded:: 1.2.0
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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