DS006593#

cBCI Matrix Multimodal Dataset

Access recordings and metadata through EEGDash.

Citation: Basak Celik, Tab Memmott, Matthew Lawhead, Srikar Ananthoju, Deniz Erdogmus (2025). cBCI Matrix Multimodal Dataset. 10.18112/openneuro.ds006593.v1.0.0

Modality: eeg Subjects: 21 Recordings: 214 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006593

dataset = DS006593(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 = DS006593(cache_dir="./data", subject="01")

Advanced query

dataset = DS006593(
    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{ds006593,
  title = {cBCI Matrix Multimodal Dataset},
  author = {Basak Celik and Tab Memmott and Matthew Lawhead and Srikar Ananthoju and Deniz Erdogmus},
  doi = {10.18112/openneuro.ds006593.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006593.v1.0.0},
}

About This Dataset#

Multimodal Sensor Fusion for EEG-Based BCI Typing Systems

Dataset Overview

This dataset contains recordings of EEG and EyeTracking for a BCI spelling task. The data were collected in 2023 at Northeastern University.

View full README

Multimodal Sensor Fusion for EEG-Based BCI Typing Systems

Dataset Overview

This dataset contains recordings of EEG and EyeTracking for a BCI spelling task. The data were collected in 2023 at Northeastern University.

N=21
Calibration task were proctored using BciPy [1]
The dataset is organized in accordance with the Brain Imaging Data Structure (BIDS) specification (version 1.7.0).

Methodology

Calibration data were collected from control participants (n=21, mean age 23.6 ± 3.1 years) in a quiet lab room at Northeastern University. EEG data were collected using the DSI-24, dry electrode cap (Wearable Sensing, San Diego CA) at a sampling rate of 300 Hz. The device employs a hardware filter permitting a collection bandwidth of 0.003–150 Hz. Data were recorded from Fp1/2, Fz, F3/4, F7/8, Cz, C3/4, T7/T8, T3/T4, Pz, P3/P4, P7/P8, T5/T6, O1/2 with linked-ear reference (A1 and A2) and ground at A1. All data were collected using a Lenovo Legion 5 Pro Laptop with Windows 11, an Intel Core i7-11800H @ 2.30 GHz, 16 GB DDR4 RAM, and a NVIDIA GeForce RTX 3050. Trigger fidelity on the experiment laptop was verified using the Matrix Time Test Task in BciPy and a photodiode. The results of this timing test were used to determine static offsets between hardware and prevent experimentation with any timing violations greater than +/− 10 ms. The Eyetracker data were collected using a portable eye tracker (Tobii Pro Nano) at a sampling rate of 60 Hz. The matrix paradigm and the data acquisition modules are developed in BciPy [1], which is a standalone application for experimental data collection. This work focuses on a specific BCI paradigm called single-character-presentation (SCP) based visual presentation, which consists of symbols presented in matrix form and individually highlighted in randomized order. Calibration task presented letter characters at a rate of 4 Hz, with 100 inquiries consisting of 10 letters each (1 target, 9 non-target). In 10% of the inquiries, only non-target characters were shown. The stimuli included all 26 letters of the English alphabet, as well as the characters “_” for space and “<“ for backspace. The order of target stimuli was randomly distributed among the inquiries. Between inquiries, there was a two-second blank screen. Each inquiry consisted of a one-second prompt showing the target letter, followed by a 0.5s fixation, and then the presentation of 10 letters. The letters were displayed in the center of the screen, in white on a black background. Target prompts and stimuli were presented in white, while fixation crosses were rendered in red.

The experimental protocol was approved by the Northeastern University Institutional Review Board (IRB). All participants provided written informed consent prior to participation.

Directory Structure

The datasets follows the BIDS convention with the following structure: /sub-[subject]/ses-[session]/[eeg or et]. To load the BIDS formatted data into BciPy Simulator, please see the following directory: /sourcedata/bcipy_metadata. This directory contains the raw BciPy parameter files. It also contains the output of the matrix display (matrix.png) for eyetracking visualization.

Contact Information

For questions or issues regarding this dataset, please contact the corresponding author Basak Celik_ via email.

[1] Memmott T, Koçanaoğulları A, Lawhead M, Klee D, Dudy S, Fried-Oken M, Oken B. BciPy: brain-computer interface software in Python. Brain-Computer Interfaces, 8(4), 137-53, 2021.

Dataset Information#

Dataset ID	`DS006593`
Title	cBCI Matrix Multimodal Dataset
Year	2025
Authors	Basak Celik, Tab Memmott, Matthew Lawhead, Srikar Ananthoju, Deniz Erdogmus
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006593.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: 21
Recordings: 214
Tasks: 1

Channels & sampling rate

Channels: 19
Sampling rate (Hz): 300.0
Duration (hours): 0.0

API Reference#

Use the DS006593 class to access this dataset programmatically.

class eegdash.dataset.DS006593(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006593. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 21; 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/ds006593 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006593

Examples

>>> from eegdash.dataset import DS006593
>>> dataset = DS006593(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