DS003846#

Prediction Error

Access recordings and metadata through EEGDash.

Citation: Lukas Gehrke, Sezen Akman, Albert Chen, Pedro Lopes, Klaus Gramann (2021). Prediction Error. 10.18112/openneuro.ds003846.v2.0.2

Modality: eeg Subjects: 19 Recordings: 325 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003846

dataset = DS003846(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS003846(cache_dir="./data", subject="01")

Advanced query

dataset = DS003846(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

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

Cite This Dataset

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

BibTeX

@dataset{ds003846,
  title = {Prediction Error},
  author = {Lukas Gehrke and Sezen Akman and Albert Chen and Pedro Lopes and Klaus Gramann},
  doi = {10.18112/openneuro.ds003846.v2.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003846.v2.0.2},
}

About This Dataset#

Readme

In case of any questions, please contact: Lukas Gehrke, lukas.gehrke@tu-berlin.de, orcid: 0000-0003-3661-1973

Overview

View full README

Readme

In case of any questions, please contact: Lukas Gehrke, lukas.gehrke@tu-berlin.de, orcid: 0000-0003-3661-1973

Overview

Cyber-Physical Systems: Prediction Error

These data were collected at https://www.tu.berlin/bpn. Data collection occurred either between 10:00 and 12:00 or between 14:00 and 18:00.

To learn about the task, independent-, dependent-, and control variables, please consult the methods sections of the following two publications:

https://dl.acm.org/doi/abs/10.1145/3290605.3300657 https://iopscience.iop.org/article/10.1088/1741-2552/ac69bc/meta

Contents of the dataset: Output from BIDS-validator

Summary 324 Files, 9.76GB 19 - Subjects 5 - Sessions

Available Tasks PredictionError

Available Modalities EEG

Quality assessment of the data: Link to data paper, once done

Methods

Subjects

The study sample consists of 19 participants (participant_id 1 to 19) with ages ranging from 18 to 34 years and varying cap sizes from 54 to 60. Stimulation is delivered in three blocks: Block_1, Block_2, and Block_3, utilizing different combinations of Visual, Vibro, and EMS.

Participant Information: Age: Ranges from 18 to 34 years. Cap Size: Varied, with sizes ranging from 54 to 60. Stimulation Blocks: Block_1 and Block_2 include Visual, Visual + Vibro, and Visual + Vibro + EMS. Block_3 primarily involves Visual + Vibro + EMS. Usage of Stimulation Blocks: Most participants experience Visual stimulation in all blocks. Visual + Vibro is common in Block_1 and Block_2. Visual + Vibro + EMS is prevalent in Block_3. Some participants did not experience certain blocks (indicated by “0”). Other Observations: Cap size variation doesn’t show a clear pattern in relation to stimulation blocks. Participants exhibit diverse stimulation patterns, showcasing individualized experiences.

Task, Environment and Variables

This set of variables outlines key parameters in a neuroscience experiment involving a haptic task. Here’s a summary:

box: Description: Represents the target object to be touched following its spawn. Units: String (presumably indicating the characteristics or identity of the object). normal_or_conflict: Description: Describes the behavior of the target object in the current trial, distinguishing between oddball and non-oddball conditions. Units: String (presumably indicating the nature of the trial). condition: Description: Indicates the level of haptic realism in the experiment. Units: String (presumably representing different levels of realism). cube: Description: Specifies the position of the target object, whether it is located on the left, right, or center. Units: String (presumably indicating spatial orientation). trial_nr: Description: Denotes the number of the current trial in the experiment. Units: Integer.

Apparatus

Here’s a summary of the recording environment:

EEG Stream Name: BrainVision
EEG Reference and Ground: FCz and AFz, respectively
EEG Channel Locations: 63 channels with specific names (e.g., Fp1, Fz, Pz) and types (EEG)
Additional Channels: 1 EOG (Electrooculogram)
Power Line Frequency: 50 Hz
Manufacturer: Brain Products
Manufacturer’s Model Name: BrainAmp DC
Cap Manufacturer: EasyCap
Cap Model Name: actiCap 64ch CACS-64
EEG Placement Scheme: Positions chosen from a 10% system
Channel Counts: - EEG Channels: 63 - EOG Channels: 1 - ECG Channels: 0 - EMG Channels: 0 - Miscellaneous Channels: 0 - Trigger Channels: 0

This configuration indicates a high-density EEG setup with specific electrode placements, utilizing Brain Products’ BrainAmp DC model. The electrode cap is manufactured by EasyCap, with the specific model name actiCap 64ch CACS-64. The EEG data is sampled at an unspecified frequency, and the system is designed to capture electrical brain activity across a comprehensive set of channels. The recording includes an additional channel for recording eye movements (EOG). Overall, the setup appears suitable for detailed EEG investigations in neurophysiological research.

The motion capture recording environment uses two devices: “rigid_head” and “rigid_handr,” which correspond to “HTCViveHead” and “HTCViveRightHand” in the BIDS (Brain Imaging Data Structure) naming convention. The tracked points include “Head” and “handR.” The motion data is captured using quaternions with channels named “quat_X,” “quat_Y,” “quat_Z,” and “quat_W.” Positional data includes channels “_X,” “_Y,” and “_Z.” The system is manufactured by HTC, with the model name “Vive,” and the recording has a sampling frequency of 90 Hz. Additional information such as software versions is not provided.

Dataset Information#

Dataset ID	`DS003846`
Title	Prediction Error
Year	2021
Authors	Lukas Gehrke, Sezen Akman, Albert Chen, Pedro Lopes, Klaus Gramann
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003846.v2.0.2
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: 19
Recordings: 325
Tasks: 1

Channels & sampling rate

Channels: 63 (50), 64 (50)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003846 class to access this dataset programmatically.

class eegdash.dataset.DS003846(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003846. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 19; recordings: 50; 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/ds003846 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003846

Examples

>>> from eegdash.dataset import DS003846
>>> dataset = DS003846(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