EEGdash›OpenNeuro›DS005540
Iss. 5540 · 59 subjects · 103 recordings · CC0
Dataset Brief · EmoEEG-MC

DS005540: eeg dataset, 59 subjects#

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Citation: Xin XU, Xinke SHEN, Xuyang CHEN, Qingzhu ZHANG, Sitian WANG, Yihan LI, Zongsheng LI, Dan ZHANG, Mingming ZHANG, Quanying LIU (20). EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding. 10.18112/openneuro.ds005540.v1.0.7

59-participant EEG dataset — EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.

Data & curation Xin XU · Xinke SHEN · Xuyang CHEN · Qingzhu ZHANG · Sitian WANG · Yihan LI · …
Year 20 · Distributed via OpenNeuro
Funding the National Key R&D Program of China (2021YFF1200804) · Shenzhen Excellent Youth Project (RCYX20231211090405003) · + 2 more
EEG · 68 ch600 Hz · mixedBIDS 1.6.0Task · emotionHealthyVisualAffect
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005540

dataset = DS005540(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005540(cache_dir="./data", subject="01")

Advanced query

dataset = DS005540(
    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{ds005540,
  title = {EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding},
  author = {Xin XU and Xinke SHEN and Xuyang CHEN and Qingzhu ZHANG and Sitian WANG and Yihan LI and Zongsheng LI and Dan ZHANG and Mingming ZHANG and Quanying LIU},
  doi = {10.18112/openneuro.ds005540.v1.0.7},
  url = {https://doi.org/10.18112/openneuro.ds005540.v1.0.7},
}
§ 02Study · The README

About This Dataset#

Xin XU[^1,†], Xinke SHEN[^1,†,*], Xuyang CHEN[^1], Qingzhu ZHANG[^1], Sitian WANG[^1], Yihan LI[^1], Zongsheng LI[^1,^2], Dan ZHANG[^3], Mingming ZHANG[^1], Quanying LIU[^1,*]

Corresponding authors: Quanying LIU (liuqy@sustech.edu.cn); Xinke SHEN (shenxk@sustech.edu.cn)

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Authors

† These authors contributed equally to this work.

Abstract

View full README

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Authors

† These authors contributed equally to this work.

Abstract

Decoding emotions using electroencephalography (EEG) is gaining increasing attention due to its objectivity in measuring emotional states. However, the ability of existing EEG-based emotion decoding methods to generalize across different contexts remains underexplored, as most approaches are trained and evaluated only within a single context. Studying emotions across multiple contexts is essential for advancing our understanding of the neural mechanisms underlying emotional processing and enhancing the real-world applicability of affective computing systems. A key limitation in this field is the lack of EEG datasets designed specifically to capture emotional responses across diverse contexts. To address this gap, we present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of a specific emotion category was validated through subjective reports.

Using Support Vector Machines (SVMs) with L1 regularization, we achieved cross-context emotion decoding accuracies of 66.7% for binary classification (positive vs. negative emotions) and 28.9% for seven-category emotion classification, both significantly above chance levels. The EmoEEG-MC dataset serves as a foundational resource for advancing cross-context emotion recognition and enhancing the real-world application of emotion decoding methods.

Dataset Description

The dataset includes EEG data from 60 participants, along with peripheral physiological data (PPG and GSR) for some participants. Among the 60 participants, sub01-sub54**have complete trials (21 imagery trials and 21 video trials), while sub55-sub60** have missing trials. The details of the missing trials are as follows: - sub55: Missing 3 imagery trials (Trials 19-21) and 3 video trials (Trials 40-42). - sub56: Missing 2 imagery trials (Trials 20 and 21). - sub57: Missing 4 imagery trials (Trials 6, 8, 13, and 21) and 6 video trials (Trials 23, 24, 36, 37, 38, and 42). - sub58: Missing 3 imagery trials (Trials 9, 20, and 21). - sub59: Missing 6 imagery trials (Trials 2, 4, 6, 12, 19, and 21) and 4 video trials (Trials 29, 37, 39, and 42). - sub60: Missing 14 imagery trials (Trials 8-21) and 12 video trials (Trials 31-42).

All missing values are denoted as n/a in the participants’ behavioral data.

Experimental Trial Reordering and Missing Trial Information

Trial Reordering

After reordering, the sequence for both imagery and video trials is as follows: reorder = ['sad4', 'sad5', 'sad8', 'dis4', 'dis5', 'dis8', 'fear4', 'fear5', 'fear8', 'neu4', 'neu5', 'neu8', 'joy4', 'joy5', 'joy8', 'ten4', 'ten5', 'ten8', 'ins4', 'ins5', 'ins8']

Full Trial Participants

For participants with complete trials (sub01-sub54, with the same order for both imagery and video trials; detailed stimulus information can be found in sub-xx/sub-xx_events), the experimental sequence is as follows: 1. ['joy5', 'ins5', 'joy8', 'fear8', 'sad8', 'dis5', 'neu4', 'neu5', 'neu8', 'ten5', 'ten8', 'joy4', 'dis4', 'fear4', 'sad4', 'ins8', 'ins4', 'ten4', 'dis8', 'fear5', 'sad5'] 2. ['fear8', 'fear5', 'dis4', 'ins8', 'joy8', 'ins4', 'neu4', 'neu8', 'neu5', 'sad4', 'dis8', 'fear4', 'ten5', 'ten8', 'joy4', 'dis5', 'sad8', 'sad5', 'joy5', 'ten4', 'ins5'] 3. ['ten4', 'joy4', 'joy8', 'neu4', 'neu8', 'neu5', 'dis5', 'fear4', 'fear5', 'ten8', 'ten5', 'ins5', 'fear8', 'dis4', 'dis8', 'ins8', 'joy5', 'ins4', 'sad4', 'sad5', 'sad8'] 4. ['fear5', 'dis8', 'dis5', 'joy4', 'ten5', 'ins5', 'neu4', 'neu8', 'neu5', 'sad8', 'fear8', 'sad4', 'ins4', 'ins8', 'joy8', 'fear4', 'sad5', 'dis4', 'ten4', 'joy5', 'ten8'] 5. ['joy8', 'ten4', 'ins5', 'fear5', 'sad5', 'dis4', 'neu4', 'neu5', 'neu8', 'joy4', 'ten8', 'joy5', 'sad4', 'dis8', 'fear8', 'ins4', 'ten5', 'ins8', 'sad8', 'dis5', 'fear4'] 6. ['joy8', 'ins5', 'ins8', 'dis4', 'dis8', 'fear8', 'ten4', 'joy5', 'ten5', 'dis5', 'fear5', 'fear4', 'ten8', 'ins4', 'joy4', 'sad8', 'sad4', 'sad5', 'neu4', 'neu5', 'neu8'] 7. ['joy8', 'ten8', 'joy4', 'fear4', 'sad5', 'dis5', 'ins5', 'ten5', 'ten4', 'dis4', 'sad8', 'dis8', 'ins4', 'ins8', 'joy5', 'sad4', 'fear8', 'fear5', 'neu4', 'neu5', 'neu8'] 8. ['neu4', 'neu5', 'neu8', 'dis8', 'sad4', 'fear5', 'ins4', 'ins5', 'ten5', 'dis4', 'sad8', 'fear4', 'ins8', 'joy4', 'ten8', 'fear8', 'dis5', 'sad5', 'ten4', 'joy8', 'joy5'] 9. ['sad5', 'fear4', 'fear8', 'joy4', 'joy8', 'ten5', 'dis8', 'dis5', 'sad4', 'neu4', 'neu8', 'neu5', 'ins8', 'ten8', 'ins4', 'sad8', 'fear5', 'dis4', 'joy5', 'ten4', 'ins5'] 10. ['sad4', 'fear5', 'sad8', 'joy8', 'ten8', 'joy4', 'sad5', 'dis8', 'fear4', 'neu4', 'neu8', 'neu5', 'ten4', 'ten5', 'ins4', 'dis4', 'fear8', 'dis5', 'joy5', 'ins5', 'ins8'] 11. ['joy4', 'ins4', 'joy5', 'fear8', 'dis8', 'sad4', 'ten8', 'ins5', 'ten5', 'sad5', 'sad8', 'fear5', 'ins8', 'ten4', 'joy8', 'neu8', 'neu4', 'neu5', 'fear4', 'dis4', 'dis5'] 12. ['sad8', 'fear5', 'fear8', 'ten8', 'ten5', 'joy8', 'fear4', 'sad4', 'sad5', 'neu4', 'neu8', 'neu5', 'ins8', 'ins4', 'ten4', 'dis5', 'dis8', 'dis4', 'joy5', 'joy4', 'ins5'] 13. ['sad8', 'dis8', 'sad4', 'ten4', 'ten8', 'ins4', 'dis5', 'fear8', 'sad5', 'ten5', 'ins5', 'joy8', 'neu4', 'neu8', 'neu5', 'fear5', 'fear4', 'dis4', 'joy4', 'joy5', 'ins8'] 14. ['ins8', 'ten4', 'ins5', 'neu4', 'neu8', 'neu5', 'sad5', 'dis4', 'sad4', 'ins4', 'ten8', 'ten5', 'dis8', 'sad8', 'fear8', 'joy5', 'joy4', 'joy8', 'fear4', 'fear5', 'dis5'] 15. ['ins8', 'ten5', 'ten8', 'sad8', 'sad4', 'sad5', 'joy4', 'ins4', 'ins5', 'fear8', 'fear5', 'fear4', 'ten4', 'joy5', 'joy8', 'neu5', 'neu4', 'neu8', 'dis4', 'dis5', 'dis8'] 16. ['fear4', 'dis4', 'fear8', 'ins8', 'joy8', 'ten8', 'dis5', 'sad4', 'dis8', 'ins5', 'ins4', 'joy4', 'neu8', 'neu4', 'neu5', 'fear5', 'sad8', 'sad5', 'joy5', 'ten5', 'ten4'] 17. ['ten5', 'ins4', 'ins8', 'dis8', 'fear4', 'sad5', 'ins5', 'joy8', 'ten4', 'sad8', 'fear8', 'fear5', 'ten8', 'joy5', 'joy4', 'sad4', 'dis5', 'dis4', 'neu5', 'neu4', 'neu8'] 18. ['neu4', 'neu5', 'neu8', 'sad4', 'dis8', 'dis5', 'joy4', 'ten4', 'ten5', 'sad5', 'fear5', 'fear4', 'ins5', 'ins4', 'ten8', 'dis4', 'fear8', 'sad8', 'joy8', 'ins8', 'joy5'] 19. ['joy5', 'ten8', 'ins4', 'fear4', 'dis8', 'sad4', 'ten5', 'joy8', 'joy4', 'sad8', 'dis5', 'fear8', 'neu8', 'neu4', 'neu5', 'ins5', 'ten4', 'ins8', 'fear5', 'dis4', 'sad5'] 20. ['joy5', 'ins8', 'joy4', 'neu4', 'neu5', 'neu8', 'fear4', 'sad4', 'fear8', 'ins5', 'ten4', 'ten5', 'dis4', 'sad8', 'sad5', 'ten8', 'ins4', 'joy8', 'dis5', 'fear5', 'dis8'] 21. ['ten8', 'joy4', 'ins5', 'sad4', 'dis4', 'fear8', 'ins8', 'joy8', 'ins4', 'neu8', 'neu4', 'neu5', 'sad5', 'sad8', 'fear5', 'ten5', 'joy5', 'ten4', 'fear4', 'dis5', 'dis8'] 22. ['joy5', 'ten8', 'ten4', 'dis4', 'fear4', 'fear5', 'joy8', 'ten5', 'joy4', 'sad5', 'sad8', 'dis8', 'neu5', 'neu8', 'neu4', 'ins4', 'ins5', 'ins8', 'fear8', 'sad4', 'dis5'] 23. ['neu4', 'neu5', 'neu8', 'dis4', 'fear4', 'sad8', 'ins8', 'joy4', 'ten8', 'fear8', 'fear5', 'sad5', 'ten4', 'ins5', 'joy8', 'dis8', 'sad4', 'dis5', 'ten5', 'joy5', 'ins4'] 24. ['joy5', 'ten5', 'ins4', 'fear4', 'sad8', 'sad4', 'ins5', 'ten4', 'ten8', 'sad5', 'fear5', 'fear8', 'ins8', 'joy8', 'joy4', 'dis8', 'dis5', 'dis4', 'neu8', 'neu4', 'neu5'] 25. ['dis8', 'dis5', 'sad4', 'ins8', 'ten4', 'joy8', 'sad8', 'fear4', 'fear8', 'joy5', 'ins4', 'ten8', 'dis4', 'fear5', 'sad5', 'neu8', 'neu5', 'neu4', 'joy4', 'ins5', 'ten5'] 26. ['fear4', 'sad5', 'fear8', 'ten4', 'ins5', 'joy8', 'dis4', 'dis8', 'sad8', 'ins4', 'joy5', 'joy4', 'dis5', 'sad4', 'fear5', 'ins8', 'ten8', 'ten5', 'neu5', 'neu8', 'neu4'] 27. ['dis4', 'dis5', 'fear4', 'ins8', 'ins4', 'joy5', 'sad8', 'fear8', 'sad5', 'ins5', 'joy4', 'ten8', 'neu4', 'neu8', 'neu5', 'fear5', 'sad4', 'dis8', 'ten4', 'ten5', 'joy8'] 28. ['ten4', 'ins5', 'joy4', 'dis5', 'sad5', 'fear4', 'ins8', 'joy8', 'ins4', 'fear5', 'fear8', 'dis8', 'neu5', 'neu8', 'neu4', 'ten8', 'joy5', 'ten5', 'sad4', 'dis4', 'sad8'] 29. ['joy5', 'ten5', 'ins5', 'neu8', 'neu4', 'neu5', 'fear5', 'sad8', 'sad5', 'joy8', 'ten8', 'joy4', 'fear8', 'fear4', 'dis4', 'ten4', 'ins8', 'ins4', 'dis8', 'dis5', 'sad4'] 30. ['sad8', 'dis8', 'dis5', 'joy5', 'ten4', 'joy4', 'sad5', 'fear5', 'fear8', 'ten8', 'ins8', 'ins4', 'sad4', 'fear4', 'dis4', 'joy8', 'ins5', 'ten5', 'neu5', 'neu8', 'neu4'] 31. ['dis4', 'dis8', 'sad4', 'neu5', 'neu4', 'neu8', 'joy5', 'ins8', 'ins4', 'fear4', 'fear8', 'sad8', 'ins5', 'ten8', 'joy4', 'sad5', 'dis5', 'fear5', 'ten4', 'joy8', 'ten5'] 32. ['joy5', 'joy4', 'ten4', 'sad5', 'fear5', 'fear4', 'ins5', 'ten8', 'ins8', 'dis8', 'dis5', 'sad8', 'ten5', 'ins4', 'joy8', 'sad4', 'fear8', 'dis4', 'neu5', 'neu8', 'neu4'] 33. ['sad5', 'dis8', 'dis5', 'ins5', 'ten5', 'ten4', 'dis4', 'fear4', 'fear5', 'ten8', 'ins8', 'joy4', 'neu5', 'neu4', 'neu8', 'fear8', 'sad4', 'sad8', 'joy5', 'joy8', 'ins4'] 34. ['ten5', 'ins5', 'joy4', 'sad4', 'fear5', 'fear4', 'ten8', 'joy8', 'ins8', 'dis8', 'sad5', 'dis5', 'joy5', 'ten4', 'ins4', 'dis4', 'fear8', 'sad8', 'neu4', 'neu8', 'neu5'] 35. ['sad4', 'fear8', 'dis4', 'ins4', 'ins8', 'joy4', 'neu8', 'neu5', 'neu4', 'sad8', 'fear4', 'dis5', 'ten4', 'ten5', 'ten8', 'sad5', 'dis8', 'fear5', 'joy8', 'ins5', 'joy5'] 36. ['joy5', 'joy4', 'joy8', 'dis4', 'dis8', 'fear5', 'neu5', 'neu8', 'neu4', 'ins4', 'ten5', 'ten4', 'dis5', 'sad5', 'fear4', 'ten8', 'ins8', 'ins5', 'sad4', 'sad8', 'fear8'] 37. ['fear4', 'dis5', 'sad5', 'neu5', 'neu4', 'neu8', 'ins8', 'joy8', 'ten5', 'fear5', 'sad4', 'fear8', 'ins4', 'joy4', 'ten8', 'dis4', 'dis8', 'sad8', 'joy5', 'ins5', 'ten4'] 38. ['joy8', 'ten8', 'ins8', 'fear8', 'sad4', 'fear5', 'ten4', 'ten5', 'joy5', 'sad8', 'dis4', 'fear4', 'neu4', 'neu5', 'neu8', 'ins5', 'ins4', 'joy4', 'sad5', 'dis8', 'dis5'] 39. ['ins4', 'ten8', 'joy4', 'neu5', 'neu8', 'neu4', 'dis8', 'fear4', 'sad8', 'ins5', 'joy8', 'ten4', 'dis5', 'dis4', 'fear5', 'ins8', 'ten5', 'joy5', 'fear8', 'sad5', 'sad4'] 40. ['ins4', 'ten4', 'ins5', 'sad5', 'dis5', 'fear4', 'neu5', 'neu8', 'neu4', 'ten5', 'ins8', 'joy4', 'sad8', 'fear5', 'sad4', 'ten8', 'joy5', 'joy8', 'dis8', 'dis4', 'fear8'] 41. ['ins5', 'ten8', 'ins4', 'dis8', 'sad4', 'dis5', 'joy8', 'ten5', 'ins8', 'neu8', 'neu4', 'neu5', 'fear8', 'dis4', 'fear5', 'joy4', 'joy5', 'ten4', 'sad5', 'sad8', 'fear4'] 42. ['ten8', 'ten4', 'joy8', 'dis8', 'sad5', 'sad4', 'joy5', 'ins8', 'ins4', 'neu4', 'neu5', 'neu8', 'fear4', 'dis4', 'fear5', 'ins5', 'ten5', 'joy4', 'dis5', 'fear8', 'sad8'] 43. ['ins5', 'ten5', 'ins4', 'neu5', 'neu8', 'neu4', 'sad4', 'dis4', 'sad5', 'ins8', 'joy8', 'joy4', 'fear8', 'fear4', 'dis8', 'ten8', 'ten4', 'joy5', 'dis5', 'sad8', 'fear5'] 44. ['sad8', 'dis5', 'dis4', 'joy5', 'ins5', 'joy8', 'sad5', 'sad4', 'fear5', 'ten4', 'ten8', 'ins4', 'neu8', 'neu5', 'neu4', 'dis8', 'fear8', 'fear4', 'joy4', 'ten5', 'ins8'] 45. ['ins5', 'joy8', 'ins8', 'fear8', 'fear5', 'sad5', 'joy5', 'ten8', 'ten5', 'neu5', 'neu4', 'neu8', 'dis5', 'dis8', 'sad4', 'ins4', 'ten4', 'joy4', 'sad8', 'dis4', 'fear4'] 46. ['fear5', 'dis5', 'dis8', 'ins5', 'ten5', 'ten8', 'neu8', 'neu4', 'neu5', 'fear8', 'dis4', 'sad4', 'ten4', 'ins8', 'ins4', 'sad5', 'sad8', 'fear4', 'joy8', 'joy4', 'joy5'] 47. ['ins4', 'joy5', 'joy8', 'sad5', 'fear5', 'dis8', 'neu8', 'neu4', 'neu5', 'ins8', 'ten4', 'joy4', 'fear8', 'dis5', 'sad8', 'ins5', 'ten8', 'ten5', 'sad4', 'dis4', 'fear4'] 48. ['joy5', 'ins8', 'ins5', 'dis8', 'dis5', 'fear5', 'ten4', 'ins4', 'joy8', 'dis4', 'fear4', 'sad5', 'ten8', 'ten5', 'joy4', 'fear8', 'sad8', 'sad4', 'neu8', 'neu4', 'neu5'] 49. ['dis4', 'sad5', 'sad4', 'neu4', 'neu8', 'neu5', 'joy4', 'ten5', 'ten8', 'dis8', 'fear8', 'dis5', 'ins4', 'joy8', 'ten4', 'fear4', 'sad8', 'fear5', 'ins8', 'ins5', 'joy5'] 50. ['ten5', 'ins8', 'ins4', 'neu4', 'neu8', 'neu5', 'fear4', 'fear8', 'dis4', 'joy4', 'ten4', 'ins5', 'fear5', 'sad5', 'dis8', 'ten8', 'joy8', 'joy5', 'sad4', 'sad8', 'dis5'] 51. ['ten8', 'joy8', 'ten5', 'dis8', 'fear5', 'dis4', 'joy5', 'ten4', 'ins4', 'fear4', 'sad4', 'dis5', 'neu8', 'neu4', 'neu5', 'ins8', 'ins5', 'joy4', 'sad5', 'fear8', 'sad8'] 52. ['joy4', 'joy5', 'ins8', 'fear5', 'dis5', 'dis8', 'neu5', 'neu4', 'neu8', 'joy8', 'ins5', 'ten5', 'sad5', 'fear4', 'dis4', 'ten4', 'ten8', 'ins4', 'sad8', 'fear8', 'sad4'] 53. ['neu8', 'neu4', 'neu5', 'dis5', 'sad4', 'fear4', 'joy5', 'ins4', 'ten4', 'fear8', 'sad5', 'sad8', 'ten8', 'joy4', 'ten5', 'fear5', 'dis4', 'dis8', 'joy8', 'ins5', 'ins8'] - sub54 Imagery sequence:

['ten8', 'ten5', 'ten4', 'fear8', 'fear5', 'fear4', 'dis8', 'dis5', 'dis4', 'joy8', 'joy5', 'joy4', 'sad8', 'sad5', 'sad4', 'neu8', 'neu5', 'neu4', 'ins8', 'ins5', 'ins4']

  • sub54 Video sequence: ['joy8', 'joy5', 'joy4', 'sad8', 'sad5', 'sad4', 'dis8', 'dis5', 'dis4', 'ins8', 'ins5', 'ins4', 'fear8', 'fear5', 'fear4', 'neu8', 'neu5', 'neu4', 'ten8', 'ten5', 'ten4']

Participants with Missing Trials

For participants with missing trials (sub55-sub60), the experimental sequences differ slightly: - sub55: The sequence for imagery and video trials is:

['dis5', 'sad4', 'fear8', 'joy4', 'joy5', 'ten8', 'fear5', 'sad8', 'sad5', 'joy8', 'ten5', 'ins8', 'dis8', 'dis4', 'fear4', 'ins5', 'ten4', 'ins4']

  • sub56: - Imagery sequence:

    ['joy8', 'joy5', 'ins4', 'sad4', 'fear5', 'dis8', 'neu4', 'neu8', 'neu5', 'ten8', 'joy4', 'ins5', 'fear4', 'dis5', 'sad8', 'ins8', 'ten5', 'ten4', 'sad5']

    • Video sequence: ['joy8', 'joy5', 'ins4', 'sad4', 'fear5', 'dis8', 'neu4', 'neu8', 'neu5', 'ten8', 'joy4', 'ins5', 'fear4', 'dis5', 'sad8', 'ins8', 'ten5', 'ten4', 'sad5', 'dis4', 'fear8']

  • sub57: - Imagery sequence:

    ['neu8', 'neu5', 'neu4', 'ins4', 'joy5', 'sad5', 'sad8', 'ins8', 'joy4', 'ten8', 'dis5', 'fear8', 'joy8', 'ins5', 'ten5', 'fear4', 'fear5']

    • Video sequence: ['neu8', 'ins4', 'joy5', 'ten4', 'sad5', 'sad4', 'sad8', 'ins8', 'joy4', 'ten8', 'dis8', 'dis5', 'ten5', 'fear4', 'fear5']

  • sub58: - Imagery sequence:

    ['sad5', 'fear5', 'sad8', 'ins8', 'joy5', 'joy4', 'sad4', 'dis8', 'neu5', 'neu8', 'neu4', 'ten8', 'joy8', 'ten4', 'fear4', 'fear8', 'dis5', 'ins5']

    • Video sequence: ['sad5', 'fear5', 'sad8', 'ins8', 'joy5', 'joy4', 'sad4', 'dis8', 'dis4', 'neu5', 'neu8', 'neu4', 'ten8', 'joy8', 'ten4', 'fear4', 'fear8', 'dis5', 'ins5', 'ten5', 'ins4']

  • sub59: - Imagery sequence:

    ['dis5', 'fear4', 'ins8', 'fear8', 'dis8', 'fear5', 'neu4', 'neu8', 'joy4', 'ten8', 'ten4', 'dis4', 'sad5', 'sad4', 'joy5']

    • Video sequence: ['dis5', 'sad8', 'fear4', 'joy8', 'ins8', 'ins5', 'fear8', 'fear5', 'neu4', 'neu8', 'neu5', 'joy4', 'ten8', 'ten4', 'sad5', 'ten5', 'joy5']

  • sub60: - Imagery sequence:

    ['neu5', 'neu4', 'neu8', 'dis5', 'sad4', 'dis4', 'ten4']

    • Video sequence: ['neu5', 'neu4', 'neu8', 'dis5', 'sad4', 'dis4', 'ten4', 'ins4', 'ten5']

Participants’ Behaviour Reports

The ten behavioral rating items for the participants are as follows: - Joy - Inspiration - Tenderness - Sadness - Fear - Disgust - Arousal - Valence - Familiarity - Liking

Channels

The EEG channels follow the 10-20 system with 64 channels, and the channel names are as follows: ‘Fp1’, ‘Fpz’, ‘Fp2’, ‘AF7’, ‘AF3’,’AF4’,’AF8’, ‘F7’, ‘F5’,’F3’,’F1’,’Fz’, ‘F2’, ‘F4’, ‘F6’, ‘F8’, ‘FT7’, ‘FC5’, ‘FC3’, ‘FC1’,’FCz’,’FC2’,’FC4’, ‘FC6’, ‘FT8’, ‘T7’,’C5’, ‘C3’, ‘C1’, ‘Cz’, ‘C2’, ‘C4’, ‘C6’, ‘T8’, ‘TP7’, ‘CP5’, ‘CP3’, ‘CP1’,’CPz’,’CP2’, ‘CP4’,’CP6’, ‘TP8’, ‘P7’,’P5’, ‘P3’, ‘P1’, ‘Pz’,’P2’, ‘P4’, ‘P6’, ‘P8’, ‘PO7’, ‘PO3’,’POz’, ‘PO4’,’PO8’, ‘O1’,’Oz’,’O2’, ‘F9’, ‘F10’, ‘TP9’, ‘TP10’ The order of the 64 channels mentioned in subsequent files follows the same order as listed above.

Preprocess Procedure

The EEG preprocessing procedures were as follows: First, the data were filtered to 0.1-47 Hz, downsampled to 200 Hz, and then segmented into trials. For imagery trials, we used the 30 seconds before the button press (or 30 seconds before the start of the rating if no button was pressed) for further analysis; for video trials, we selected the last 30 seconds of the video clip presentation for further analysiscite{shen_contrastive_2023,hu_eeg_2017}. Next, we inspected bad channels based on two criteria. First, channels containing more than 30% outliers were flagged, where outliers are defined as absolute values exceeding three times from the trial’s median of absolutecite{DECHEVEIGNE2018903}. Second, we identified channels with abnormal variance by plotting the variance for each channel across trials to detect significant variance jumps. Suspected bad channels were further verified through visual inspection of the EEG signals and were subsequently interpolated using the average of three neighboring channels. Then we performed Independent Component Analysis (ICA) and manually removed components derived from eye movements and muscle artifacts. Finally, common average referencing and trial reordering were applied. As the order of materials presentation was randomized across subjects, reordering of the trials ensured that the order of EEG data was the same for all subjects to facilitate subsequent analysis.

Our dataset also provides several commonly used EEG features, including differential entropy (DE) and power spectral density (PSD) features. DE and PSD features were extracted from the preprocessed data within each non-overlapping second at five frequency bands (delta band: 1-4 Hz, theta band: $4-8 mathrm{~Hz}$, alpha band: $8-14 mathrm{~Hz}$, beta band: $14-30 mathrm{~Hz}$, and gamma band: $30-47 mathrm{~Hz}$ ). The formula to calculate DE and PSD followed the practice in the SEED dataset : $$ begin{gathered} P S D=Eleft[x^2right] \ D E=frac{1}{2} ln left(2 pi e sigma^2right) end{gathered} $$ where $x$ is the EEG signal filtered into a frequency band and $sigma^2$ is the variance of the EEG signal.

Guide for labels

  • Using Preprocessed Data

If you prefer to work with preprocessed data, navigate to the following directories: \derivatives\sub-idx\ses-ima\eeg or \derivatives\sub-idx\ses-vid\eeg.

Here, you will find: - _task-emotion_de.npy - _task-emotion_psd.npy - _task-emotion_reorder.npy

These files have been preprocessed and reordered in the following sequence: **sad-dis-fear-neu-joy-ten-ins**. For example: - The 1st to 3rd stimuli correspond to sad4, sad5, and sad8. - The 4th to 6th stimuli correspond to dis4, dis5, and dis8, and so on.

Each session (ima or vid) typically includes **21 trials**. For information on participants with missing trials, refer to the **Participants with Missing Trials** section above. - Preprocessing Data on Your Own

If you’d like to preprocess the data yourself, follow these steps: 1. Locate Raw Data:

  • The raw EEG data is in the directory: sub-idx\eeg\sub-idx_task-emotion_eeg.edf.

  • Triggers are marked directly in the .edf file’s notations.

  1. Map Triggers to Trial Types: - Mapping information between ‘TypeID’ in .edf file’s notations and trial categories is here:

    sub-01: vid-31 ima-30 fade-28 rating-29 sub-02: vid-33 rating-31 ima-32 fade-30 sub-03: ima-32 rating-31 vid-33 fade-30 sub-04: ima-50 rating-49 fade-48 vid-51 sub-05: vid-6 rating-4 ima-5 fade-3 sub-06: ima-23 fade-21 rating-22 vid-24 sub-07: vid-51 rating-49 ima-50 fade-48 sub-08: ima-41 rating-40 vid-42 fade-37 sub-09: ima-5, fade-3, rating-4, vid-6 sub-10: ima-23 rating-22 fade-21 vid-24 sub-11: ima-23 rating-22 fade-21 vid-24 sub-12: ima-32 fade-30 rating-31 vid-33 sub-13: vid-24 rating-22 ima-23 fade-21 sub-14: vid-24 rating-22 ima-23 fade-21 sub-15: ima-5 fade-3 rating-4 vid-6 sub-16: vid-24 rating-22 ima-23 fede-21 sub-17: vid-6 rating-4 ima-5 fade-3 sub-18: vid-6 rating-4 ima-5 fade-3 sub-19: vid-6 rating-4 ima-5 fade-3 sub-20: vid-6 rating-4 ima-5 fade-3 sub-21: vid-36 rating-34 ima-35 fade-33 sub-22: vid-56 rating-54 ima-55 fade-53 sub-23: vid-36 rating-34 ima-35 fade-33 sub-24: vid-36 rating-34 ima-35 fade-33 sub-25: vid-6 rating-4 ima-5 fade-3 sub-26: vid-6 rating-4 ima-5 fade-3 sub-27: vid-6 rating-4 ima-5 fade-3 sub-28: vid-16 rating-14 ima-15 fade-13 sub-29: vid-26 rating-24 ima-25 fade-23 sub-30: vid-26 rating-24 ima-25 fade-23 sub-31: vid-6 rating-4 ima-5 fade-3 sub-32: vid-6 rating-4 ima-5 fade-3 sub-33: vid-15 rating-13 ima-14 fade-12 sub-34: vid-6 rating-4 ima-5 fade-3 sub-35: vid-6 rating-4 ima-5 fade-3 sub-36: vid-6 rating-4 ima-5 fade-3 sub-37: vid-6 rating-4 ima-5 fade-3 sub-38: vid-16 rating-14 ima-15 fade-13 sub-39: vid-9 rating-7 ima-8 fade-6 sub-40: vid-6 rating-4 ima-5 fade-3 sub-41: vid-56 rating-54 ima-55 fade-53 sub-42: vid-26 rating-24 ima-25 fade-23 sub-43: vid-38 rating-36 ima-37 fade-35 sub-44: vid-16 rating-14 ima-15 fade-13 sub-45: vid-16 rating-14 ima-15 fade-13 sub-46: vid-10 rating-8 ima-9 fade-7 sub-47: vid-10 rating-8 ima-9 fade-7 sub-48: vid-6 rating-4 ima-5 fade-3 sub-49: vid-6 rating-4 ima-5 fade-3 sub-50: vid-16 rating-14 ima-15 fade-13 sub-51: vid-26 rating-24 ima-25 fade-23 sub-52: vid-6 rating-4 ima-5 fade-3 sub-53: vid-38 rating-33 ima-36 fade-34 sub-54: vid-6 rating-4 ima-5 fade-3 sub-55: vid-30 rating-28 ima-29 fade-27 sub-56: vid-6 rating-4 ima-5 fade-3 sub-57: vid-6 rating-4 ima-5 fade-3 sub-58: vid-26 rating-24 ima-25 fade-23 sub-59: vid-26 rating-24 ima-25 fade-23 sub-60: vid-36 rating-34 ima-35 fade-33 (tips: For many participants, triggers end with “vid-6 rating-4 ima-5 fade-3”, so you can %10 to get the same sequence.)

  2. Segment Data: - Based on the trigger-trial mapping, segment the data accordingly.

  3. Reorder Trials: - Use the sequence provided in the Trial Reordering section above to rearrange the trials in your preferred order.

This approach allows flexibility for custom analyses while ensuring alignment with the established trial order.

Endtime of original EEG files

subject date endtime1 endtime2 01 2023/8/24 16:33 02 2023/8/20 18:34 03 2023/8/23 22:52 04 2023/8/27 18:02 05 2023/8/28 22:24 06 2023/9/5 17:51 07 2023/9/6 19:29 21:53 08 2023/9/9 15:41 18:02 09 2023/9/12 21:41 10 2023/9/16 17:11 18:10 11 2023/9/19 21:48 12 2023/9/26 20:52 22:02 13 2023/9/27 20:40 22:07 14 2023/9/28 17:18 15 2023/10/1 22:12 16 2023/10-4 20:35 22:21 17 2023/10/5 20:14 22:24 18 2023/10/13 21:05 22:25 19 2023/10/14 11:41 12:51 20 2023/10/15 21:04 22:06 21 2024/4/14 20:19 21:32 22 2024/4/15 16:41 18:47 23 2024/4/18 15:44 17:31 24 2024/4/25 17:15 18:08 25 2024/5/3 16:53 18:37 26 2024/5/9 15:48 17:49 27 2024/5/10 20:34 22:11 28 2024/5/11 20:22 22:04 29 2024/5/12 16:07 18:05 30 2024/5/16 15:58 17:39 31 2024/5/17 20:55 22:26 32 2024/5/19 20:07 21:56 33 2024/5/23 20:54 34 2024/5/26 16:01 17:47 35 2024/5/29 20:50 22:35 36 2024/5/31 20:47 22:00 37 2024/6/6 16:13 17:08 38 2024/6/20 17:52 19:31 39 2024/6/26 21:47 40 2024/7/2 22:17 23:08 41 2024/7/3 20:44 21:51 42 2024/7/4 20:14 21:43 43 2024/7/9 15:45 17:40 44 2024/7/10 16:27 17:55 45 2024/7/11 15:45 17:35 46 2024/7/12 15:24 17:20 47 2024/7/14 16:36 18:23 48 2024/7/15 15:55 17:29 49 2024/7/16 16:20 17:49 50 2024/7/17 16:24 18:20 51 2024/7/18 16:13 17:39 52 2024/7/21 20:20 21:36 53 2023/8/8 21:40 54 2024/7/22 15:27 16:59 55 2023/8/16 22:48 56 2023/8/26 18:01 57 2024/4/21 20:34 22:12 58 2024/5/15 19:49 22:06 59 2024/6/27 20:25 22:00 60 2024/7/5 20:00

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=59, range 18–26 yr, mean 21.0 yr)

152025
Other · 59

Sex composition

60
subjects
Female
23
Male
37
F : M ratio
0.62 : 1
38% female · n = 60 subjects with reported sex.

Channel counts: 68 ch (n=103 recordings)

Sampling frequencies (Hz)

6001200

Total recording duration: 167 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 68 ch · EEG · 600 Hz · mixed · 59 subjects, 103 recordings
Live trace viewer — sub-13 · task-emotion · run-02

Showing one representative recording out of 59 subjects and 103 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — DS005540
HED event descriptors word cloud HED event descriptors word cloud — DS005540
§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS005540

Title

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Author (year)

Xin2024

Canonical

—

Importable as

DS005540, Xin2024

Year

20

Authors

Xin XU, Xinke SHEN, Xuyang CHEN, Qingzhu ZHANG, Sitian WANG, Yihan LI, Zongsheng LI, Dan ZHANG, Mingming ZHANG, Quanying LIU

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005540.v1.0.7

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005540,
  title = {EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding},
  author = {Xin XU and Xinke SHEN and Xuyang CHEN and Qingzhu ZHANG and Sitian WANG and Yihan LI and Zongsheng LI and Dan ZHANG and Mingming ZHANG and Quanying LIU},
  doi = {10.18112/openneuro.ds005540.v1.0.7},
  url = {https://doi.org/10.18112/openneuro.ds005540.v1.0.7},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005540(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Xin2024
Canonical—
Importable asDS005540 · Xin2024
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005540(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding

Study:

ds005540 (OpenNeuro)

Author (year):

Xin2024

Canonical:

—

Also importable as: DS005540, Xin2024.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 59; recordings: 103; 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/ds005540 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005540 DOI: https://doi.org/10.18112/openneuro.ds005540.v1.0.7 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005540
>>> dataset = DS005540(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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds005540 · pull with datasets.load_dataset("EEGDash/ds005540").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005540.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds005540 to reproduce the tutorial on this dataset.

Citation

Xin XU, Xinke SHEN, Xuyang CHEN, Qingzhu ZHANG, Sitian WANG, … (20). EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding. 10.18112/openneuro.ds005540.v1.0.7

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds005540.v1.0.7.

BIDS
BIDS 1.6.0
Sidecars
eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#