DS007655: eeg dataset, 32 subjects#

MorseEEG-ATP

Access recordings and metadata through EEGDash.

Citation: HZY, Research Team (2026). MorseEEG-ATP. 10.18112/openneuro.ds007655.v1.0.0

Modality: eeg Subjects: 32 Recordings: 64 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007655

dataset = DS007655(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 = DS007655(cache_dir="./data", subject="01")

Advanced query

dataset = DS007655(
    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{ds007655,
  title = {MorseEEG-ATP},
  author = {HZY and Research Team},
  doi = {10.18112/openneuro.ds007655.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007655.v1.0.0},
}

About This Dataset#

MorseEEG-ATP: a multitask EEG dataset for auditory temporal processing and listening ability assessment using Morse code

Introduction

MorseEEG-ATP is a publicly available multitask electroencephalography (EEG) dataset for studying auditory temporal processing and listening ability assessment using Morse code (MC) stimuli. Auditory temporal processing refers to the ability of the auditory system to perceive, encode, and integrate the temporal structure of sounds. The dataset was designed to address the relative lack of open resources targeting the intermediate stage between low-level acoustic encoding and higher-level language or cognitive processing, namely the extraction and discrimination of stable temporal patterns from discrete acoustic elements governed by explicit temporal rules. MC provides a controlled and parameterizable stimulus model for this purpose because it consists of short and long acoustic elements combined according to fixed rules, has a clear and stable temporal structure, and depends relatively little on lexical or semantic knowledge in untrained listeners.

Dataset Content

The dataset contains synchronously recorded EEG and behavioral data from 32 healthy adult participants (14 females). Data were collected with a 62-channel EEG system at a sampling rate of 600 Hz and are organized in an EEG-BIDS compatible structure (BIDS v1.10.1). The release includes: 1. Raw EEG recordings in BrainVision format (.vhdr, .vmrk, .eeg) together with BIDS sidecar files.

View full README

MorseEEG-ATP: a multitask EEG dataset for auditory temporal processing and listening ability assessment using Morse code

Introduction

MorseEEG-ATP is a publicly available multitask electroencephalography (EEG) dataset for studying auditory temporal processing and listening ability assessment using Morse code (MC) stimuli. Auditory temporal processing refers to the ability of the auditory system to perceive, encode, and integrate the temporal structure of sounds. The dataset was designed to address the relative lack of open resources targeting the intermediate stage between low-level acoustic encoding and higher-level language or cognitive processing, namely the extraction and discrimination of stable temporal patterns from discrete acoustic elements governed by explicit temporal rules. MC provides a controlled and parameterizable stimulus model for this purpose because it consists of short and long acoustic elements combined according to fixed rules, has a clear and stable temporal structure, and depends relatively little on lexical or semantic knowledge in untrained listeners.

Dataset Content

The dataset contains synchronously recorded EEG and behavioral data from 32 healthy adult participants (14 females). Data were collected with a 62-channel EEG system at a sampling rate of 600 Hz and are organized in an EEG-BIDS compatible structure (BIDS v1.10.1). The release includes: 1. Raw EEG recordings in BrainVision format (.vhdr, .vmrk, .eeg) together with BIDS sidecar files. 2. Minimally preprocessed EEG data under derivatives/preprocessed/ for reproducible downstream analysis. 3. Trial-aligned event annotations, including event onsets, trigger values, block labels, speed conditions, and stimulus file references. 4. Behavioral measures including accuracy and reaction time, together with participant-level summary information. 5. Companion preprocessing and benchmark analysis scripts.

Experimental Design

Tasks: The experiment includes two auditory discrimination tasks that share the same stimulus set and trial structure but differ in decision rules. - Dot-count task (``task-dotcount``): Participants report the number of dots contained in the presented Morse code character. - First-last match task (``task-firstlast``): Participants judge whether the first and last elements of the presented Morse code character are identical.
Presentation-rate conditions: Each task includes three presentation-rate conditions defined by Morse character transmission speed: 15, 30, and 40 characters per minute (CPM). These conditions support comparisons across different temporal-rate demands.
Rationale: By jointly manipulating task rules and presentation rate, the dataset supports comparative analyses of auditory temporal processing, temporal pattern discrimination, and listening ability assessment across processing demands and speed conditions.
Trial procedure: Each trial consists of a fixation period, stimulus presentation, a response window, and a jittered inter-trial interval.

Data Acquisition

EEG System: g.tec medical engineering GmbH g.HIamp.
Sampling Rate: 600 Hz.
Online Filtering: 0.1-100 Hz bandpass filter and a 48-52 Hz notch filter.
Effective Channels: 62 scalp electrodes arranged according to the International 10-20 system.
Reference Electrode: right earlobe.
Stimulus and behavior software: Stimuli were presented and behavioral responses were collected using E-Prime 3.0.

Research Value

The value of MorseEEG-ATP can be summarized at three levels. First, as an experimental paradigm, it combines discrete acoustic units and explicit temporal rules within a unified and reproducible framework for studying auditory temporal processing. Second, as an assessment resource, it combines multitask conditions, cross-rate manipulations, trial-aligned EEG, and behavioral measures, supporting the development and validation of listening ability assessment methods. Third, as a shared dataset, it provides raw and minimally preprocessed EEG, event annotations, behavioral measures, and analysis scripts in a standardized format, enabling reproducibility, method evaluation, and cross-study comparison.

Technical Validation Highlights

According to the accompanying paper, technical validation demonstrates the validity and usability of the dataset from three aspects: behavioral performance, event-related potential components, and listening ability assessment.

Data Quality Notes

Data corruption: - Channel 56 data for participant sub-13 are corrupted. - Channel 56 data for participant sub-14 are corrupted. - Channel 43 data for participant sub-15 are corrupted.
In the minimally preprocessed data, these corrupted channels have been interpolated using neighboring electrodes.

Usage Recommendations

Citation: Please cite the associated Scientific Data article when using this dataset.
Preprocessing: The raw data preserve complete trigger markers. The companion preprocessing pipeline can be used directly or adapted for related studies.
Analysis: The derivative .mat files are organized for efficient comparison across blocks and presentation-rate conditions.
Applications: The dataset supports studies of auditory temporal processing, temporal pattern discrimination, rhythm perception, individual differences, listening ability assessment, EEG analysis methods, neural decoding, and related auditory brain-computer interface approaches under cross-condition settings.

Summary

MorseEEG-ATP provides a standardized and shareable EEG resource for studying auditory temporal processing with controlled Morse code stimuli. It bridges a gap between low-level acoustic encoding datasets and higher-level language datasets by targeting the extraction and discrimination of temporal patterns governed by explicit rules. The resource supports research on temporal pattern discrimination, individual differences, and listening ability assessment, while also providing a useful benchmark for EEG analysis and decoding methods.

Dataset Information#

Dataset ID	`DS007655`
Title	MorseEEG-ATP
Author (year)	—
Canonical	—
Importable as	`DS007655`
Year	2026
Authors	HZY, Research Team
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007655.v1.0.0
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: 32
Recordings: 64
Tasks: 2

Channels & sampling rate

Channels: 62
Sampling rate (Hz): 600.0
Duration (hours): Not calculated

API Reference#

Use the DS007655 class to access this dataset programmatically.

class eegdash.dataset.DS007655(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

MorseEEG-ATP

Study:: ds007655 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007655, nan.

Modality: eeg. Subjects: 32; recordings: 64; tasks: 2.

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/ds007655 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007655 DOI: https://doi.org/10.18112/openneuro.ds007655.v1.0.0

Examples

>>> from eegdash.dataset import DS007655
>>> dataset = DS007655(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