DS006647#

Poetry Assessment EEG Dataset 2

Access recordings and metadata through EEGDash.

Citation: Soma Chaudhuri, Joydeep Bhattacharya (2025). Poetry Assessment EEG Dataset 2. 10.18112/openneuro.ds006647.v1.0.1

Modality: eeg Subjects: 4 Recordings: 22 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006647

dataset = DS006647(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 = DS006647(cache_dir="./data", subject="01")

Advanced query

dataset = DS006647(
    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{ds006647,
  title = {Poetry Assessment EEG Dataset 2},
  author = {Soma Chaudhuri and Joydeep Bhattacharya},
  doi = {10.18112/openneuro.ds006647.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006647.v1.0.1},
}

About This Dataset#

Understanding how the brain engages with poetic language is key to advancing empirical research on aesthetic and creative cognition. This experiment involved 64-channel EEG recordings and behavioural ratings from 51 participants who read and evaluated 210 short English-language texts — 70 Haiku (nature-themed), 70 Senryu (emotion-themed), and 70 non-poetic Control texts. Each poem/text was rated on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativity — using a 7-point scale.

The full study involved 51 participants, and the data were divided into two BIDS-compliant datasets to ensure technical validation and facilitate upload to OpenNeuro.

Poetry Assessment EEG Dataset 1 contains data from 47 participants whose continuous EEG recordings passed technical validation and were used in the primary analyses.

Poetry Assessment EEG Dataset 2 (this dataset) includes the remaining 4 participants (P105, P141, P142, P146), whose EEG recordings were acquired in segments due to session interruptions and later concatenated during preprocessing. These participants were excluded from the PSD analysis to avoid potential artifacts but are included here for completeness and transparency. In this dataset, the participants.tsv file maps anonymized BIDS IDs (sub-001 to sub-004) to the original participant codes used during data collection (P105–P146), as follows:

sub-001 → P105 sub-002 → P141

View full README

Understanding how the brain engages with poetic language is key to advancing empirical research on aesthetic and creative cognition. This experiment involved 64-channel EEG recordings and behavioural ratings from 51 participants who read and evaluated 210 short English-language texts — 70 Haiku (nature-themed), 70 Senryu (emotion-themed), and 70 non-poetic Control texts. Each poem/text was rated on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativity — using a 7-point scale.

The full study involved 51 participants, and the data were divided into two BIDS-compliant datasets to ensure technical validation and facilitate upload to OpenNeuro.

Poetry Assessment EEG Dataset 1 contains data from 47 participants whose continuous EEG recordings passed technical validation and were used in the primary analyses.

Poetry Assessment EEG Dataset 2 (this dataset) includes the remaining 4 participants (P105, P141, P142, P146), whose EEG recordings were acquired in segments due to session interruptions and later concatenated during preprocessing. These participants were excluded from the PSD analysis to avoid potential artifacts but are included here for completeness and transparency. In this dataset, the participants.tsv file maps anonymized BIDS IDs (sub-001 to sub-004) to the original participant codes used during data collection (P105–P146), as follows:

sub-001 → P105 sub-002 → P141 sub-003 → P142 sub-004 → P146

Dataset Structure and Navigation: Each subject folder contains four core EEG files:

channels.tsv – EEG channel metadata eeg.json – EEG recording metadata eeg.set – Raw EEG data (EEGLAB format) events.tsv – Event markers aligned with poem presentation

The /code/ directory includes:

Preprocessing.m – MATLAB preprocessing script BioSemi64.loc – 64-channel coordinate file

The /derivatives/ directory contains:

Behavioural_Ratings/ – One .csv file per participant (e.g., P105.csv), including trial-by-trial ratings across five dimensions: Aesthetic Appeal, Vivid Imagery, Emotional Impact (labeled as ‘being moved’), Originality, and Creativity.

Psychometric_Responses/ – A single .csv file with demographic and trait-level questionnaire responses per participant, including: PANAS (mood), Openness, Curiosity, VVIQ (visual imagery), AVIQ (auditory imagery), MAAS (mindfulness), and AReA (aesthetic responsiveness).

Also includes questionnaires.pdf with full questionnaire texts and scoring keys

The /stimuli/ directory includes:

All 210 texts used in the experiment: 70 Haiku (nature-themed poetry), 70 Senryu (emotion-themed poetry), 70 Control (non-poetic matched prose).

Block-wise trial assignments for all seven blocks

Resting-state EEG was recorded at the beginning and end of each session. These segments are embedded within the raw EEG files and can be identified using the following trigger codes in events.tsv:

65285, 65286 → Resting state (before experiment); 65287, 65288 → Resting state (after experiment)

Interested users are encouraged to consult Poetry Assessment EEG Dataset 1 to gain a complete understanding of the full experiment and its validated main dataset. All preprocessing steps, event markers, and metadata structures were applied identically across both datasets (Poetry Assessment EEG Dataset 1 and Poetry Assessment EEG Dataset 2), ensuring consistency. This enables users to apply their own quality control pipelines and include these data if desired.

Of note, the anonymized participant IDs (e.g., PXXX) are used consistently across all data modalities, enabling reliable cross-referencing between EEG data, behavioural ratings, and psychometric responses. Data collection took place at the Department of Psychology at Goldsmiths, University of London, UK. The project was approved by the Local Ethics Committee at the Department of Psychology, Goldsmiths University of London. The experiment was conducted in accordance with the Declaration of Helsinki.

All EEG, behavioural, and psychometric data were anonymized. Participant identifiers were coded (P101–P151), and no names, dates of birth, or other direct identifiers are included.

Dataset Information#

Dataset ID	`DS006647`
Title	Poetry Assessment EEG Dataset 2
Year	2025
Authors	Soma Chaudhuri, Joydeep Bhattacharya
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006647.v1.0.1
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: 4
Recordings: 22
Tasks: 1

Channels & sampling rate

Channels: 70
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS006647 class to access this dataset programmatically.

class eegdash.dataset.DS006647(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006647. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 4; recordings: 4; 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/ds006647 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006647

Examples

>>> from eegdash.dataset import DS006647
>>> dataset = DS006647(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