BNCI 2014-008 P300 dataset (ALS patients)

BNCI 2014-008 P300 dataset (ALS patients).

Dataset Overview

• Code: BNCI2014-008
• Paradigm: p300
• DOI: 10.3389/fnhum.2013.00732
• Subjects: 8
• Sessions per subject: 1
• Events: Target=2, NonTarget=1
• Trial interval: [0, 1.0] s
• File format: Unknown
• Data preprocessed: True

Acquisition

• Sampling rate: 256.0 Hz
• Number of channels: 8
• Channel types: eeg=8
• Channel names: Fz, Cz, Pz, Oz, P3, P4, PO7, PO8
• Montage: 10-10
• Hardware: g.MOBILAB
• Software: BCI2000
• Reference: right earlobe
• Ground: left mastoid
• Sensor type: active electrodes
• Line frequency: 50.0 Hz
• Online filters: 0.1-10 Hz bandpass, 50 Hz notch
• Electrode type: g.Ladybird
• Electrode material: Ag/AgCl

Participants

• Number of subjects: 8
• Health status: ALS patients
• Clinical population: amyotrophic lateral sclerosis
• Age: mean=58.0, std=12.0, min=40, max=72
• Gender distribution: M=5, F=3
• BCI experience: naive
• Species: human

Experimental Protocol

• Paradigm: p300
• Number of classes: 2
• Class labels: Target, NonTarget
• Study design: P300 speller with 6x6 matrix for copy-spelling task in ALS patients
• Feedback type: visual
• Stimulus type: row-column intensification
• Stimulus modalities: visual
• Primary modality: visual
• Synchronicity: synchronous
• Mode: online
• Training/test split: True
• Instructions: Copy spell seven predefined words of five characters each by focusing attention on desired letters

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: 36
• Number of repetitions: 10
• Inter-stimulus interval: 125.0 ms
• Stimulus onset asynchrony: 250.0 ms

Data Structure

• Trials: 35
• Blocks per session: 7
• Trials context: per subject (7 words, 5 characters each)

Preprocessing

• Data state: preprocessed
• Preprocessing applied: True
• Steps: bandpass filtering, notch filtering, artifact rejection, baseline correction
• Highpass filter: 0.1 Hz
• Lowpass filter: 10.0 Hz
• Bandpass filter: {'lowcutoffhz': 0.1, 'highcutoffhz': 10.0}
• Notch filter: [50] Hz
• Filter type: Butterworth
• Filter order: 4
• Artifact methods: amplitude threshold rejection
• Re-reference: right earlobe
• Epoch window: [0.0, 1.0]
• Notes: Epochs with peak amplitude >70 μV or <-70 μV were rejected. Baseline correction based on 200 ms preceding each epoch.

Signal Processing

• Classifiers: SWLDA
• Feature extraction: temporal features, decimation

Cross-Validation

• Method: 7-fold
• Folds: 7
• Evaluation type: within_subject

Performance (Original Study)

• Accuracy: 97.5%
• Binary Accuracy Offline: 87.4
• P300 Amplitude Mean Uv: 3.3

BCI Application

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

Tags

• Pathology: ALS
• Modality: P300
• Type: ERP

Documentation

• DOI: 10.3389/fnhum.2013.00732
• License: CC-BY-NC-ND-4.0
• Investigators: Angela Riccio, Luca Simione, Francesca Schettini, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti Belardinelli, Donatella Mattia, Febo Cincotti
• Senior author: Febo Cincotti
• Contact: a.riccio@hsantalucia.it
• Institution: Fondazione Santa Lucia
• Department: Neuroelectrical Imaging and BCI Laboratory
• Address: Via Ardeatina, 306, 00179 Rome, Italy
• Country: Italy
• Repository: BNCI Horizon
• Publication year: 2013
• Funding: Italian Agency for Research on ALS-ARiSLA project 'Brindisys'; FARI project C26I12AJZZ at the Sapienza University of Rome
• Ethics approval: Fondazione Santa Lucia ethic committee
• Keywords: brain computer interface, amyotrophic lateral sclerosis, P300, attention, working memory

References

Riccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O., & Mattia, D. (2013). Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Frontiers in human neuroscience, 7, 732. https://doi.org/10.3389/fnhum.2013.00732

Notes

.. note::

`BNCI2014_008 was previously named BNCI2014008. BNCI2014008` 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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