BNCI 2015-012 PASS2D P300 dataset

BNCI 2015-012 PASS2D P300 dataset.

Dataset Overview

• Code: BNCI2015-012
• Paradigm: p300
• DOI: 10.3389/fnins.2011.00099
• Subjects: 10
• Sessions per subject: 1
• Events: Target=1, NonTarget=2
• Trial interval: [0, 0.8] s
• Runs per session: 2
• Session IDs: session_1
• File format: gdf
• Data preprocessed: True
• Contributing labs: Berlin Institute of Technology, Fraunhofer FIRST

Acquisition

• Sampling rate: 250.0 Hz
• Number of channels: 63
• Channel types: eeg=63
• Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F10, F2, F3, F4, F5, F6, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8
• Montage: 10-20
• Hardware: Brain Products
• Software: Matlab
• Reference: nose
• Sensor type: wet Ag/AgCl electrodes
• Line frequency: 50.0 Hz
• Online filters: 0.1-250 Hz analog bandpass, then 40 Hz lowpass
• Cap manufacturer: EasyCap GmbH
• Cap model: Fast'n Easy Cap
• Electrode type: wet Ag/AgCl electrodes
• Electrode material: Ag/AgCl
• Auxiliary channels: EOG (1 ch)

Participants

• Number of subjects: 10
• Health status: patients
• Clinical population: Healthy
• Age: mean=25.1, min=21, max=34
• Gender distribution: male=9, female=3
• BCI experience: mostly naive
• Species: human

Experimental Protocol

• Paradigm: p300
• Task type: auditory ERP speller
• Number of classes: 2
• Class labels: Target, NonTarget
• Tasks: text spelling, counting task
• Study design: Nine-class auditory ERP paradigm with predictive text entry system (PASS2D). Users focus attention on two-dimensional auditory stimuli varying in pitch (high/medium/low) and direction (left/middle/right) presented via headphones.
• Study domain: communication
• Feedback type: visual
• Stimulus type: auditory tones
• Stimulus modalities: auditory, visual
• Primary modality: auditory
• Synchronicity: synchronous
• Mode: online
• Training/test split: True
• Instructions: Focus on target stimuli while ignoring all non-target stimuli. Minimize eye movements and muscle artifacts. Count targets during calibration. Spell sentences during online phase.

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
• Stimulus frequencies: [708.0, 524.0, 380.0] Hz
• Number of targets: 9
• Number of repetitions: 15
• Inter-stimulus interval: 125.0 ms
• Stimulus onset asynchrony: 225.0 ms

Data Structure

• Trials: 27
• Trials context: total across all calibration runs (3 runs × 9 trials per run)

Preprocessing

• Data state: filtered and downsampled
• Preprocessing applied: True
• Steps: analog bandpass filter, lowpass filter, downsampling, artifact rejection
• Highpass filter: 0.1 Hz
• Lowpass filter: 40.0 Hz
• Bandpass filter: {'lowcutoffhz': 0.1, 'highcutoffhz': 250.0}
• Filter type: analog bandpass then digital lowpass
• Artifact methods: threshold rejection
• Re-reference: nose
• Downsampled to: 100.0 Hz
• Epoch window: [-0.15, 0.8]
• Notes: Epochs with peak-to-peak voltage difference exceeding 100 μV in any channel were rejected during calibration. No artifact correction applied in online runs.

Signal Processing

• Classifiers: FDA, Fisher discriminant analysis
• Feature extraction: mean amplitude in discriminative intervals
• Spatial filters: shrinkage regularization

Cross-Validation

• Method: cross-validation
• Evaluation type: within_session

Performance (Original Study)

• Accuracy: 72.5%
• Itr: 3.4 bits/min
• Characters Per Minute: 0.8
• Spelling Speed Chars Per Min: 0.8

BCI Application

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

Tags

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

Documentation

• Description: A novel 9-class auditory ERP paradigm driving a predictive text entry system
• DOI: 10.3389/fnins.2011.00099
• Associated paper DOI: 10.3389/fnins.2011.00112
• License: CC-BY-NC-ND-4.0
• Investigators: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann
• Senior author: Michael Tangermann
• Contact: j.hoehne@tu-berlin.de
• Institution: Berlin Institute of Technology
• Department: Machine Learning Laboratory
• Address: Franklinstr. 28/19, 10587 Berlin, Germany
• Country: Germany
• Repository: BNCI Horizon
• Publication year: 2011
• Keywords: brain–computer interface, BCI, auditory ERP, P300, N200, spatial auditory stimuli, T9, user-centered design

Abstract

Brain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.

Methodology

Participants performed a single session lasting 3-4 hours consisting of calibration phase and online spelling task. Calibration: 3 runs (plus 1 practice run), each with 9 trials covering all 9 stimuli as targets. Each trial had 13-14 pseudo-random sequences of all 9 auditory stimuli (108 subtrials total, 12 target + 96 non-target). Online spelling: 2 runs spelling German sentences using T9-style predictive text system with 9-class decisions. Each trial consisted of 135 subtrials (15 iterations of 9 stimuli). Binary classification using linear FDA with shrinkage regularization on 2-4 amplitude values per channel from discriminative intervals (N200 at 230-300ms and P300 at 350+ ms). Multiclass decision based on one-sided t-test with unequal variances across 15 classifier outputs per key.

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

.. 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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