BNCI 2015-003 P300 dataset.
- 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
- 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)
- 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
- 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
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
- Detected paradigm: p300
- Number of targets: 6
- Stimulus onset asynchrony: 175.0 ms
- Trials: 48
- Trials per class: calibration_per_direction=8
- Trials context: calibration_phase
- 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.
- Classifiers: LDA, linear binary classifier
- Feature extraction: spatio-temporal features, r2 coefficient, interval averaging
- Spatial filters: shrinkage regularization (Ledoit-Wolf)
- Method: online
- Evaluation type: online
- 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
- Applications: speller, communication
- Environment: laboratory
- Online feedback: True
- Pathology: Healthy
- Modality: Auditory
- Type: ERP, P300
- 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
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).
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.
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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