NM000188: eeg dataset, 10 subjects#

BNCI 2014-009 P300 dataset

Access recordings and metadata through EEGDash.

Citation: P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti (2013). BNCI 2014-009 P300 dataset.

Modality: eeg Subjects: 10 Recordings: 30 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000188

dataset = NM000188(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000188(cache_dir="./data", subject="01")

Advanced query

dataset = NM000188(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000188,
  title = {BNCI 2014-009 P300 dataset},
  author = {P Aricò and F Aloise and F Schettini and S Salinari and D Mattia and F Cincotti},
}

About This Dataset#

BNCI 2014-009 P300 dataset

BNCI 2014-009 P300 dataset.

Dataset Overview

  • Code: BNCI2014-009

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/11/3/035008

View full README

BNCI 2014-009 P300 dataset

BNCI 2014-009 P300 dataset.

Dataset Overview

  • Code: BNCI2014-009

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/11/3/035008

  • Subjects: 10

  • Sessions per subject: 3

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 0.8] s

  • File format: MAT

  • Data preprocessed: True

Acquisition

  • Sampling rate: 256.0 Hz

  • Number of channels: 16

  • Channel types: eeg=16

  • Channel names: Fz, Cz, Pz, Oz, P3, P4, PO7, PO8, F3, F4, FCz, C3, C4, CP3, CPz, CP4

  • Montage: 10-10

  • Hardware: g.USBamp

  • Software: BCI2000

  • Reference: linked earlobes

  • Ground: right mastoid

  • Sensor type: Ag/AgCl

  • Line frequency: 50.0 Hz

  • Online filters: bandpass 0.1-20 Hz

  • Impedance threshold: 10.0 kOhm

  • Cap manufacturer: Electro-Cap International, Inc.

Participants

  • Number of subjects: 10

  • Health status: healthy

  • Age: mean=26.8, std=5.6

  • Gender distribution: female=10, male=0

  • BCI experience: experienced

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: spelling

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 16.0 s

  • Study design: P300-based BCI with two interfaces: P300 Speller (overt attention) and GeoSpell (covert attention). 36 alphanumeric characters presented. Eight stimulation sequences per trial with 16 target intensifications.

  • Feedback type: none

  • Stimulus type: visual_intensification

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: offline

  • Training/test split: False

  • Instructions: Subject focused on one out of 36 different characters. At the beginning of each trial, the system prompted the subject with the character to attend. Target prompt appeared during a 2 s pre-trial interval.

  • Stimulus presentation: stimulus_duration_ms=125, isi_ms=125, soa_ms=250, n_sequences=8, n_intensifications_per_target=16, pre_trial_interval_s=2.0, tti_min_ms=500

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: 8

  • Inter-stimulus interval: 125.0 ms

  • Stimulus onset asynchrony: 250.0 ms

Data Structure

  • Trials: 18

  • Blocks per session: 3

  • Trials context: 6 trials × 3 runs per session

Preprocessing

  • Data state: preprocessed

  • Preprocessing applied: True

  • Steps: bandpass filtering

  • Highpass filter: 0.1 Hz

  • Lowpass filter: 20.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 20.0}

  • Filter type: Butterworth

  • Filter order: 8

  • Re-reference: linked earlobes

  • Epoch window: [0.0, 0.8]

  • Notes: EEG acquired using g.USBamp amplifier (g.Tec, Austria), digitized at 256 Hz

Signal Processing

  • Classifiers: LDA, SWLDA

  • Feature extraction: Wavelet, Time-Frequency, CWT

  • Frequency bands: analyzed=[1.0, 20.0] Hz

Cross-Validation

  • Method: cross-validation

  • Folds: 3

  • Evaluation type: within_session

Performance (Original Study)

  • P300 Latency Jitter Correlation: negative correlation with accuracy

BCI Application

  • Applications: communication, spelling

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: P300, ERP

Documentation

  • Description: Complete record of P300 evoked potentials recorded with BCI2000 using two different paradigms: P300 Speller (overt attention) and GeoSpell (covert attention). 10 healthy subjects focused on one out of 36 different characters.

  • DOI: 10.1088/1741-2560/11/3/035008

  • Associated paper DOI: 10.3389/fnhum.2013.00732

  • License: CC-BY-NC-ND-4.0

  • Investigators: P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti

  • Senior author: F Cincotti

  • Contact: p.arico@hsantalucia.it

  • Institution: Fondazione Santa Lucia IRCCS

  • Department: Neuroelectrical Imaging and BCI Lab

  • Address: Rome, Italy

  • Country: Italy

  • Repository: BNCI Horizon

  • Publication year: 2014

  • Ethics approval: Approved by local Ethics Committee

  • Keywords: P300 latency jitter, brain-computer interface, covert attention, wavelet analysis, single epoch

Abstract

This dataset represents a complete record of P300 evoked potentials recorded with BCI2000 using two different paradigms: a paradigm based on the P300 Speller originally described by Farwell and Donchin in overt attention condition and a paradigm based on the GeoSpell interface used in covert attention condition. In these sessions, 10 healthy subjects focused on one out of 36 different characters. The objective was to predict the correct character in each of the provided character selection epochs.

Methodology

Ten healthy subjects (10 female, mean age = 26.8 ± 5.6) with previous experience with P300-based BCIs attended 4 recording sessions. Scalp EEG potentials were measured using 16 Ag/AgCl electrodes arranged on an elastic cap per the 10-10 standard. Each electrode was referenced to the linked earlobes and grounded to the right mastoid. The EEG was acquired using a g.USBamp amplifier (g.Tec, Austria), digitized at 256 Hz, high pass- and low pass-filtered with cutoff frequencies of 0.1 Hz and 20 Hz, respectively. The electrode impedance did not exceed 10 kΩ. Visual stimulation, acquisition and online classification were performed with BCI2000. Each subject attended 4 recording sessions. During each session, the subject performed three runs with each of the stimulation interfaces. Each trial consisted of eight stimulation sequences, and thus, 16 intensifications of the target character. Each stimulus was intensified for 125 ms, with an inter stimulus interval (ISI) of 125 ms, yielding a 250 ms lag between the appearance of two stimuli (SOA). Pseudorandom stimulation sequences were assembled so that each target intensification would not occur within 500 ms after the previous one to avoid the attentional blink phenomenon.

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_009 was previously named BNCI2014009. BNCI2014009 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 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000188

Title

BNCI 2014-009 P300 dataset

Author (year)

Arico2014

Canonical

BNCI2014_009_P300

Importable as

NM000188, Arico2014, BNCI2014_009_P300

Year

2013

Authors

P Aricò, F Aloise, F Schettini, S Salinari, D Mattia, F Cincotti

License

CC-BY-NC-ND-4.0

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 10

  • Recordings: 30

  • Tasks: 1

Channels & sampling rate

  • Channels: 16

  • Sampling rate (Hz): 256.0

  • Duration (hours): 1.6335611979166669

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 70.9 MB

  • File count: 30

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000188 class to access this dataset programmatically.

class eegdash.dataset.NM000188(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2014-009 P300 dataset

Study:

nm000188 (NeMAR)

Author (year):

Arico2014

Canonical:

BNCI2014_009_P300

Also importable as: NM000188, Arico2014, BNCI2014_009_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 30; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000188 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000188

Examples

>>> from eegdash.dataset import NM000188
>>> dataset = NM000188(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#