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# ROAMM: Reading Observed At Mindless Moments **ROAMM** is a large-scale multimodal dataset featuring simultaneous **EEG and eye-tracking** data collected during naturalistic reading with **span-level mind-wandering annotations**. ROAMM provides a benchmark dataset for MW detection and EEG-to-text decoding tasks, and enables the study of attention-related degradation in language decoding from brain activity in naturalistic reading. ## Dataset Status * **Raw EEG (BIDS):** **Work in Progress.**

Participants

1

Size

224 MB

Updated

Jun 6, 2026

# LDAEP and resting-state EEG in healthy women ## The dataset at a glance - 69 participants, all female. - Age range: 19-40 years, mean age 25.3 years (SD 4.2). - A single recording session comprising two paradigms: LDAEP (54 participants) and resting-state (eyes-open and eyes-closed; 69 participants). - 64 EEG channels and 4 auxiliary oculogram channels. - Signal sampling rate: 2048 Hz. - Additional data include hormonal contraceptive use, menstrual cycle phase, depressive symptoms (BDI-II),

Participants

69

Size

34.6 GB

Updated

Jun 6, 2026

Resting-State EEG and Trait Anxiety ==================================== This dataset contains resting-state EEG recordings from 51 participants, collected as part of a study examining the relationship between resting-state EEG alpha/theta power, oscillatory dynamics, and trait anxiety. Participants ------------ 51 right-handed undergraduate students (25 female) from the University of Alberta, aged 17-51 years (mean = 20.4, SD = 4.9), participated for course credit. Authors ------- Tamari Sha

Participants

51

Size

7.01 GB

Updated

Jun 6, 2026

# EPOC July 2023 - July 2025 ## Project discription A project to investigate neurophysiological correlates of Post-COVID-19 by the means of EEG. ## Contact Person M. Sc. Christian Neumann Neurology Department University Medical Center Schleswig-Holstein, Campus Kiel Arnold-Heller-Straße 3 24105 Kiel Germany E-Mail: [email protected] ## Supervisors * Prof. Dr. med. Walter Maetzler * Dr. Julius Welzel ## ### Overview ##### Psychomotor vigilance Task (PVT

Participants

97

Size

21.7 GB

Updated

Jun 6, 2026

Participants

31

Size

7.34 GB

Version

v1.0.1

Updated

May 16, 2026

# EEG-Speech Brain Decoding Dataset ## Overview This dataset contains EEG recordings and audio data. ## Sessions Sessions are labeled by recording date in YYYYMMDD format. - Example: `ses-20240401` = recorded on April 1, 2024 Multiple recordings on the same day are distinguished by run numbers: - `run-N`: Nth recording of the day ## Tasks - **speechopen**: Overt speech production task - Participants vocalize visually presented text ## File Format Notes ### EEG Data Raw EEG data is stored

Participants

3

Size

49.6 GB

Updated

Jun 6, 2026

BigP3BCI Study P is a P300-based brain-computer interface dataset comprising EEG recordings from 19 healthy subjects across 2 sessions each, using a 9x8 character grid spelling paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz with standardized 10-20 montage, annotated with target and non-target visual stimulus events. This derivative dataset is part of the largest public P300 BCI dataset collection, and is optimized for machine learning applications in BCI research.

Participants

19

Size

1.46 GB

Version

v1.0.1

Updated

Jun 3, 2026

A multi-day, high-quality EEG dataset for motor imagery brain-computer interface research comprising 51 healthy subjects performing left and right hand motor imagery tasks across three sessions, with an additional 11 subjects performing 3-class motor imagery (left hand, right hand, foot). The dataset includes 59 EEG channels sampled at 1000 Hz with standardized 10-05 electrode montage, totaling 39,600 trials (51 subjects × 3 sessions × 200 trials for 2-class + 11 subjects × 3 sessions × 300 trials for 3-class) with visual and auditory cues. This resource is designed to support the development and benchmarking of motor imagery BCI algorithms and classifiers.

Participants

51

Size

63.4 GB

Version

v1.0.1

Updated

Jun 3, 2026

HEFMI-ICH is a hybrid EEG-fNIRS motor imagery dataset designed for brain-computer interface applications in intracerebral hemorrhage rehabilitation. The dataset comprises 37 participants (17 healthy controls and 20 ICH patients) performing 2-class hand motor imagery tasks (left and right hand grasping) across 1-6 sessions per subject. With 32-channel EEG recordings at 256 Hz and approximately 3,330 trials, this dataset supports the development and evaluation of BCI systems for clinical stroke rehabilitation.

Participants

37

Size

2.58 GB

Version

v1.0.1

Updated

Jun 3, 2026

A derivative 4-class code-VEP brain-computer interface dataset processed from the original Zenodo dataset (10.5281/zenodo.8255618), comparing burst c-VEP and m-sequence stimulation paradigms at two amplitude depths (100% and 40%). The study evaluates classification performance and user experience using 32-channel EEG recorded at 500 Hz from 12 healthy participants. Burst c-VEP achieved superior accuracy of 95.6% (with 52.8s calibration) and 90.5% (with 17.6s calibration), compared to m-sequence performance of 85.0% and 71.4% respectively. CNN-based decoding with 250ms sliding windows enabled these results. This derivative dataset optimizes stimulus design for reactive BCI applications while maintaining visual comfort, with reduced amplitude (40%) showing minimal accuracy loss (94.2%) while substantially improving user experience.

Participants

12

Size

148 MB

Version

v1.0.0

Updated

Jun 3, 2026