Discover datasets

# Delineating neural contributions to EEG-based speech decoding ## Overview 128-channel EEG recordings during speech production tasks. Participants produced one of 5 color words (green, magenta, orange, violet, yellow) under three speech conditions: overt, minimally overt, and covert. Each trial consists of 5 repetitions of the same word (1.25 sec per repetition). ## Channel layout (139 channels total) - Channels 1-128: EEG - Channels 129-130: DISPLAY (bipolar pair, misc) - Channels 131-132:

Participants

3

Size

1.62 GB

Updated

Jun 6, 2026

Participants

25

Size

88.6 GB

Version

v1.0.0

Updated

May 16, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing a 4-class steady-state visually evoked potential (SSVEP) brain-computer interface task. The dataset comprises 3,024 trials across 63 sessions with 64-channel EEG recordings plus 1 EOG and 1 stimulus channel at 1000 Hz sampling rate, designed to investigate ocular activity patterns and their relationship to BCI performance across multiple paradigms.

Participants

11

Size

2.06 GB

Version

v1.0.1

Updated

Jun 3, 2026

A multimodal neuroimaging dataset combining EEG and eye-tracking recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants imagining left and right hand movements in response to visual cues. Recorded at 1000 Hz using a 64-channel Neuroscan system, this dataset supports brain-computer interface research and motor imagery analysis.

Participants

31

Size

9.22 GB

Version

v1.0.1

Updated

Jun 3, 2026

A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery and motor execution tasks. The dataset comprises 2,520 trials across 63 sessions with 64-channel EEG recordings at 1000 Hz sampling rate, focusing on left and right hand grasping imagery. This resource supports the development and benchmarking of brain-computer interface (BCI) systems for motor control applications.

Participants

31

Size

4.65 GB

Version

v1.0.0

Updated

Jun 3, 2026

A motor imagery EEG dataset comprising 31 healthy participants performing left and right hand grasping imagery tasks across multiple sessions. The dataset includes 2,520 trials recorded at 1000 Hz using a 64-channel Neuroscan system with standardized electrode montage, designed for brain-computer interface research and motor control applications.

Participants

31

Size

4.71 GB

Version

v1.0.0

Updated

Jun 3, 2026

# Dataset Description ## Overview This dataset contains EEG recordings from a study investigating neural activity changes before and after an intervention. The data are organized following the Brain Imaging Data Structure (BIDS) specification. The dataset includes multiple participant groups and timepoints: - Group 1, Group 2, Group 3 - Pre-intervention (pre) and Post-intervention (post) ## Participants Participants are labeled using anonymized IDs (e.g., sub-001, sub-002, etc.). Demographic

Participants

67

Size

686 MB

Updated

Jun 6, 2026

A code-modulated visual evoked potential (cVEP) brain-computer interface dataset from 30 healthy participants performing an offline BCI task with a calculator grid stimulus. This derivative dataset demonstrates a calibration-free approach that progressively reduces training data requirements while maintaining classification performance, ultimately achieving zero-training BCI operation through neural encoding models and reconvolution techniques.

Participants

30

Size

1.26 GB

Version

v1.0.0

Updated

Jun 3, 2026

This dataset comprises EEG recordings from 15 healthy subjects performing an auditory brain-computer interface task based on the ASME (Auditory Stream segregation Multiclass ERP) paradigm. The study investigates two strategies for achieving four-class classification: ASME-4stream (four independent streams with single target stimuli) and ASME-2stream (two streams with dual target stimuli each). EEG data were recorded at 1000 Hz from 64 channels and analyzed using event-related potential methods and linear discriminant analysis classification, achieving accuracies of 83% and 86% respectively.

Participants

15

Size

13.9 GB

Version

v1.0.1

Updated

Jun 2, 2026

Participants

12

Size

3.48 GB

Version

v1.0.0

Updated

Jun 2, 2026