The datset is comprised of 46(22 commercial adertisement and 24 kannada Music clips) different subjcets EEG data recorded uisng 2 channel EEG device

The dataset folder contaions two sub folder 1. comercial advertisement 1.1 Channel_1(Ch_1) and Channel_2 (Ch_2) :Prefontal Cortex 2. Kannada Musical clips 2.1 channel_1(Ch_1) and Channel_2 (Ch_2) :left Brain

Excel file information : Each file column represneted as number of subjects and row is represnted as features per subjects There are totaly 12 excel files from two channels ( 6 for commercial advertisemnt and 6 for kannda Musical clips).

Subjective self-rating scale

Name
age Gender Have you ever had any health issues? YES NO Have you watched this song/advertisement before? YES NO Please let us know if this advertisement brings up any specific memories for you. YES NO Please Rate the following query from 1 to 10. How funny was the advertisement you watched How sad was the advertisement you watched How Horror was the advertisement you watched How relaxed was the Music you viewed with How Sad was the Music you viewed with How enjoyable was the Music you viewed with Do you think what you just watched was entertaining enough? If you have any comment please write here

Here is the website address for each stimulus that we considered:

ad1: https://www.youtube.com/watch?v=ZzG7duipQ7U&ab_channel=perfettiindia ad2: https://www.youtube.com/watch?v=SfAxUpeVhCg&ab_channel=bo0fhead ad3: https://www.youtube.com/watch?v=HqGsT6VM8Vg&ab_channel=kiddlestix song1: https://www.youtube.com/hashtag/kgfchapter2 song 2: https://www.youtube.com/watch?v=x43w4lLS9E0&ab_channel=AnandAudio Song 3: https://youtube.com/watch?v=Ysf4QRrcLGM&si=EnSIkaIECMiOmarE

For a more comprehensive understanding of the dataset and its background, we kindly ask researchers to refer to our associated manuscript titled:

Entertainment Based Database for Emotion Recognition from EEG Signals, the research article accepted at 3rd International Conference on Applied Intelligence and informatics (AII2023) held in Fostering reproducibility of research results right 29 -31 OCT 2023, DUBAI, UAE. (When utilizing this dataset in your research, please consider citing the following reference)

The EEG Motor Movement/Imagery Dataset provides 64-channel electroencephalography recordings collected with the BCI2000 system during real and imagined motor tasks. Participants completed 14 experimental runs, including two one-minute baseline recordings with eyes open and eyes closed, followed by three repetitions of four two-minute task conditions. These conditions involved either executing or imagining unilateral fist movements in response to left/right visual targets, or executing or imagining bilateral fist or foot movements in response to top/bottom visual targets. EEG signals were recorded according to the international 10-10 electrode placement system at a sampling rate of 160 Hz and are provided in EDF+ format with accompanying annotation channels. Event labels identify rest periods and task onsets using three codes: T0 for rest, T1 for left-fist or both-fists movement/imagery depending on the run type, and T2 for right-fist or both-feet movement/imagery. The dataset supports research in brain-computer interfaces, motor imagery classification, movement-related EEG dynamics, and the development of signal-processing and machine-learning methods for neural decoding.

EEGEmotions-27 Dataset

EEGEmotions-27 is a dataset of raw EEG recordings collected during affective elicitation experiments. It contains data from 88 participants as they experienced 27 distinct emotional states while watching emotionally evocative video clips.

📁 Dataset Structure

EEG Raw Data

• Location: eeg_raw/
• File Format: {Participant_ID}_{Emotion_ID}.0.txt
  • Example: 12_5.0.txt → Participant 12, Emotion 5 (Anger)
• Data Format: Plain text EEG time series (14 channels from Emotiv X headset, sampled at 256Hz)

Emotion ID Mapping

👤 Participant Information

• File: participants_info.csv
• Fields:
  • Participant ID
  • Gender:
    • 1 = Male
    • 2 = Female
  • Age:
    • 2 = 20s
    • 3 = 30s
    • 4 = 40s
    • 5 = 50s
    • 6 = 60s
  • Nation:
    • 1 = Korean
    • 2 = Vietnamese

📌 Other Files

• emotivX_channels_location.ced → Electrode position file for Emotiv X EEG headset (14 channels).

• video_clips/ → Contains video stimuli used for emotion elicitation.

📜 Citation

If you use this dataset in your research, please cite the following papers:

1. Survey Paper:

Phuong, H.T., Im, E.T., Oh, M.S., & Gim, G.Y. (2025). EEG-Based Emotion Recognition: A Review and Emerging Paths. IEEE Access, 13, 165037-165060. DOI: https://doi.org/10.1109/ACCESS.2025.3610918

@ARTICLE{11168214,
 author={Huy-Tung, Phuong and Eun-Tack, Im and Myeong-Seok, Oh and Gwang-Yong, Gim},
 journal={IEEE Access}, 
 title={EEG-Based Emotion Recognition: A Review and Emerging Paths}, 
 year={2025},
 volume={13},
 number={},
 pages={165037-165060}
}

2. Dataset Paper:

Phuong, H.T., Im, E.T., Oh, M.S., & Gim, G.Y. (2025). EEGEmotions-27: A Large-Scale EEG Dataset Annotated With 27 Fine-Grained Emotion Labels. IEEE Access, 13, 176915-176932. DOI: https://doi.org/10.1109/ACCESS.2025.3620677

@ARTICLE{11202184,
 author={Huy-Tung, Phuong and Eun-Tack, Im and Myeong-Seok, Oh and Gwang-Yong, Gim},
 journal={IEEE Access}, 
 title={EEGEmotions-27: A Large-Scale EEG Dataset Annotated With 27 Fine-Grained Emotion Labels}, 
 year={2025},
 volume={13},
 number={},
 pages={176915-176932}
}

⚖️ License

This dataset is licensed under CC BY-NC 4.0 You are free to use, share, and adapt the data for non-commercial purposes, with proper attribution.

📞 Contact

For questions or collaboration, please contact huytungst@gmail.com.

The neural basis of object recognition and semantic knowledge have been the focus of a large body of research but given the high dimensionality of object space, it is challenging to develop an overarching theory on how brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. Traditional image databases are based on manually selected object concepts and often single images per concept. In contrast, ‘big data’ stimulus sets typically consist of images that can vary significantly in quality and may be biased in content. To address this issue, recent work developed THINGS: a large stimulus set of 1,854 object concepts and 26,107 associated images (https://things-initiative.org/). In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to all concepts and 22,248 images in the THINGS stimulus set. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

This repository contains the code that was used to perform the analyses described in this paper:

Grootswagers, T., Zhou, I., Robinson, A.K. et al. Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Sci Data 9, 3 (2022). https://doi.org/10.1038/s41597-021-01102-7

• THINGS images and concept descriptions obtained from: https://osf.io/jum2f (see also: https://things-initiative.org/)

• The raw data, preprocessed data, and grand-average RDMs are publicly available on Openneuro: https://openneuro.org/datasets/ds003825

• RDMs for single subjects are publicly available on figshare: https://doi.org/10.6084/m9.figshare.14721282 (note: OSF sometimes incorrectly lists this as private)

see the README in the code folder for instructions on how to reproduce the figures in the paper.

The Neurotech EEG Dataset is a large clinical scalp EEG corpus comprising 23,607 EEG recordings from 4,914 patients acquired by a single EEG monitoring service provider between 2021 and 2025, totaling 212,186 hours of signal data (10.2 TB). A distinguishing feature is the large proportion of ambulatory recordings acquired in patients' homes, including multi-day studies — a real-world, out-of-hospital recording context largely absent from existing large clinical EEG corpora, which are predominantly hospital-based. Recordings span routine outpatient EEGs, ambulatory monitoring, and continuous inpatient/ICU EEG, all acquired with Natus/Xltek NeuroWorks hardware at 256 Hz using the standard International 10-20 montage. The dataset includes 226,486 technician-placed annotations — including 50,482 spike markers, 6,892 seizure markers, 21,330 sharp-wave annotations, and free-text clinical observations. De-identified patient-level clinical metadata (demographics, ICD-10 referral diagnoses, comorbidities, medications, EEG findings, and monitoring summaries) is included for the 4,812 patients with available clinical records. Data are released in BIDS-EEG format with HIPAA-compliant de-identification including per-patient date shifting and automated name scrubbing.

Electroencephalography (EEG) is a technique for measuring the electrical activity of the brain in the form of action potentials using electrodes placed on the scalp. The technique is gaining popularity for research investigations due to its non-invasive nature and ease of application. EEG exposes a wide range of human brain potentials, including event-related, sensory, and visually evoked potentials (VEPs), and helps to build complex applications. The current dataset consists of thirty-two subjects' EEG recordings in response to visual stimuli (VEPs). The purpose of collecting such data is because of its contribution in the advancement of visual decoding and supporting EEG-based image classification and reconstruction. The primary goal is to investigate the cognitive mechanisms behind known and unknown perceptions. The dataset was collected using a standardised experimental setup that included several experimental phases to capture the essence of the experiment. Thirty-five adult participants participated in the data collection process. They had no visual impairment and took the Vividness of Visual Imagery Questionnaire (VVIQ) test to answer sixteen questions based on their memory and imagination. Out of the thirty-five participants, thirty-two cleared the test and their EEG were recorded. The data was collected using a 14-channel EPOC X – 14 EEG device. The recordings were sampled at 128 Hz, and the 10 – 20 system was followed for electrode placement. EMOTIVPro software was used for collection and annotation. The brain activity signals were collected while the participants were viewing an image displayed on a white screen. The image consists of natural objects like apple (class A), flower (class F), car (class C) and human face (class P). The file “VVIQuestionnaire.pdf” is the questionnaire used to ascertain the visual imagination of the participants. The other file “Participant_info.csv” contains the details of the participants (age, gender, image class viewed, and Participant ID) and their VVIQ score. The names of the participants have been purposely removed for reasons of anonymity and a unique participant ID has been assigned to each participant. These IDs are further used to represent the EEG of the participants. Each class folder further contains two subfolders: A1, A2 (for class A); C1, C2 (for class C); P1, P2 (for class P); and F1, F2 (for class F). All these folders contain the data acquired from the different participants who were shown these images as a csv and edf file. This file structure makes data easier to access and analyse based on the class of visual stimuli images and experimental design employed.

This dataset has collected for the study of "Robust Detection of Event-Related Potentials in a User-Voluntary Short-Term Imagery Task.

• Project name: ANN4EEG • Project home page: https://cmi.to/ann4eeg

Experimental protocol

Rats were randomized and administered various anticonvulsants at the maximum single therapeutic dose (conversion factor from humans to rats used in this study was 5.9 as recommended) or subconvulsive dose for substances with proconvulsant activity. After reaching the peak concentration (depending on the pharmacokinetic properties of the drug) under control of the operator, brain activity was recorded for 10 min.

Substances

Diazepam (6 mg/kg, po) Phenazepam (1 mg/kg, po) Chloral hydrate (100 mg/kg, ip) Pregabalin (60 mg/kg, po) Gabapentin (360 mg/kg, po) Carbamazepine (200 mg/kg, po) Eslicarbazepine (160 mg/kg, po) Corazol (pentylenetetrazole; 20 mg/kg, ip) Picrotoxin (2 mg/kg, ip) Pilocarpine (60 mg/kg, ip) Arecoline (40 mg/kg, ip)

i-EEG Recording

A laboratory electroencephalograph (NVX-36; MKS, Moscow, Russian Federation) was used to record bioelectrical activity. Intracranial EEG signals were recorded at a sampling rate of 500 Hz, in a bipolar montage. Electrode impedance < 5 kΩ.

i-EEG montage: Olfactory bulbs (OB) (ground); P3-A1 (channel 1); O1-A1 (channel 2); P4-A2 (channel 3); O2-A2 (channel 4).

📝 Description

The EEG-Motion Cognitive State Assessment Dataset (EEG-MCSAD) is a multimodal dataset developed for research in remote neurocognitive assessment of elderly and disabled patients. It integrates simulated Electroencephalogram (EEG) data with skeletal motion tracking features, enabling comprehensive cognitive state evaluation such as Alzheimer's disease, Parkinson's disease, vascular dementia, mild cognitive impairment (MCI), HEALTH CONTROL.

The dataset reflects realistic clinical scenarios where patients perform structured cognitive tasks. EEG signals are represented through Power Spectral Density (PSD) features across different brainwave frequency bands, while motion signals capture physical responses such as velocity, acceleration, and balance stability. Additionally, a fused feature score is included to represent the combined contribution of EEG and motion signals.

The dataset is labeled into three cognitive states:

0 = Normal

1 = Mild Impairment

2 = Severe Decline

It is suitable for building and testing machine learning and deep learning models for early detection of cognitive disorders, brain-inspired computing, and assistive healthcare technologies.

🔑 Key Features

Multimodal Representation – Combines EEG and motion data for enriched cognitive assessment.

EEG Features – Power Spectral Density (PSD) values across delta, theta, alpha, beta, and gamma bands.

Motion Features – Joint velocity, joint acceleration, and stability indicators for movement analysis.

Task Performance Scores – Simulated task outcomes representing cognitive performance.

Feature Fusion – Vector-level fused feature score combining EEG and motion signals.

Target Column – Neurocognitive_State with three labeled classes (Normal, Mild Impairment, Severe Decline).

Size & Format – 1345 rows × 11 columns in CSV format.

Applications – Suitable for classification tasks, cognitive decline prediction, multimodal learning, and rehabilitation system design.

This dataset provides a structured collection of synthetically generated EEG signals designed to support research in automatic stroke detection, classification, and neurodiagnostic modeling. It includes four distinct EEG categories—Ischemic Stroke, Hemorrhagic Stroke, Transient Ischemic Attack (TIA), and Normal Control—represented across 2000 EEG trials.

Each EEG segment is captured as 16 channels, sampled at 256 Hz, with a duration of 4 seconds (1024 samples). The synthetic signals incorporate realistic neurophysiological characteristics such as delta–theta slowing (ischemic), burst-suppression patterns (hemorrhagic), intermittent desynchronization (TIA), and alpha rhythm preservation (normal), enabling the dataset to closely resemble real clinical EEG conditions.

The data is formatted into four separate CSV files, one for each category, making it directly usable for:

EEG classification

Deep learning (EEGNet, CNN, LSTM, Transformer)

Feature extraction studies (PSD, CWT, STFT)

Signal processing and biomedical research

Benchmarking machine learning models

Research papers, thesis work, and simulation studies

This dataset serves as a valuable resource for researchers developing early-stage stroke prediction systems, healthcare analytics models, and EEG-based diagnostic tools.

"N" for non-match).

This dataset consists of EEG recordings and Brain-Computer Interface (BCI) data from 25 different human subjects performing BCI experiments. More information can be found in the corresponding manuscript:

Dylan Forenzo, Yixuan Liu, Jeehyun Kim, Yidan Ding, Taehyung Yoon, Bin He: “Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control”, IEEE Transactions on Biomedical Engineering (10.1109/TBME.2023.3298957).

Please cite this paper if you use any data included in this dataset.

The dataset was collected under the support of NIH grants AT009263, EB021027, EB029354, NS096761, NS124564 to Dr. Bin He at Carnegie Mellon University.

Each file is a MATLAB object (.mat file) which contains data from a single run of BCI control. The MATLAB files are grouped into folders based on the Subject, one for each of the 25 subjects studied. Each subject completed 5 sessions of BCI experiments and each session consisted of either 18 (sessions 1 and 2) or 15 (sessions 3-5) runs, for a total of 81 runs per subject or 2025 total BCI runs.

Each of the MATLAB files contains a single structure with the following fields:

data: An array containing the EEG recordings with the size (channels x time points)

times: A vector containing the timestamps in milisceonds with the size (1 x time points)

fs: sampling frequency (1000 Hz)

labels: A cell array containing the label for each channel

targets: A list of target codes. For LR: 1 is right, 2 is left. For UD: 1 is up, 2 is down. For 2D: 1 is right, 2 is left, 3 is up, and 4 is down

event: A structure of events from BCI2000. Each index corresponds to the start of a trial and includes the time (latency) of when the trial starts, and how long each trial lasted (duration).

results: A vector of which target was hit for each trial (0 if the trial was aborted before a target was hit)

outcome: A vector indicating the outcome of each trail (1: hit, 0: abort, -1: miss)

subject: The coded subject number

session: The session number. Please note that the session numbers are for specific tasks, so even though 2D sessions began on the third day of experiments, the 2D runs are listed as session 1, 2, and 3 as they are the first, second, and third 2D sessions.

axis: The axis of control. Either LR (horizontal only, Left-Right), UD (vertical only, Up-Down), or 2D (both horizontal and vertical control).

task: The control paradigm used. Options are MI (motor imagery), OSA (overt spatial attention), MIOSA (MI and OSA together), MIOSA1 (MI controls horizontal axis, OSA controls vertical. Referred to as MI/OSA in the paper), or MIOSA2 (MI controls vertical axis, OSA controls horizontal. Referred to as OSA/MI in the paper).

run: The run number

Title: EEG Dataset for Sleep and Awake States

Description: This dataset contains electroencephalography (EEG) recordings from individuals in both sleep and awake states. The data includes multiple EEG channels commonly used in sleep research, with time-series readings capturing neural activity patterns. Each sample is labeled based on whether the individual was asleep or awake during the recording.

The dataset contains Electroencephalography (EEG) responses from 20 Indian participants, on 12 songs of different genres (from Indian Classical to Goth Rock). Each session indicates a song by its number.

For the experiment, the participants were indicated to close their eyes indicated by a single beep, and the song was presented to them on speakers. After listening to each song, a double beep was presented, asking them to open their eyes and rate their familiarity and enjoyment to the song. The responses were taken on a scale of 1 to 5, where 1 meant most familiar or most enjoyable, and 5 meant least familiar or least enjoyable.

The events timeline in segmented data must be ignored as these are inherited from rawdata

Fragility Multi-Center Retrospective Study

This dataset was updated and prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in [1].

Summary

iEEG and EEG data from 5 centers is organized in our study with a total of 100 subjects. We publish 4 centers' dataset here due to data sharing issues.

Acquisitions include ECoG and SEEG. Each run specifies a different snapshot of EEG data from that specific subject's session. For seizure sessions, this means that each run is a EEG snapshot around a different seizure event.

For additional clinical metadata about each subject, refer to the clinical Excel table in the publication.

Data Availability

NIH, JHH, UMMC, and UMF agreed to share. Cleveland Clinic did not, so requires an additional DUA.

All data, except for Cleveland Clinic was approved by their centers to be de-identified and shared. All data in this dataset have no PHI, or other identifiers associated with patient. In order to access Cleveland Clinic data, please forward all requests to Amber Sours, SOURSA@ccf.org:

Amber Sours, MPH Research Supervisor | Epilepsy Center Cleveland Clinic | 9500 Euclid Ave. S3-399 | Cleveland, OH 44195 (216) 444-8638

You will need to sign a data use agreement (DUA).

Sourcedata

For each subject, there was a raw EDF file, which was converted into the BrainVision format with mne_bids.

Each subject with SEEG implantation from Cleveland Clinic, also has an Excel table, called electrode_layout.xlsx, which outlines where the clinicians marked each electrode anatomically. Note that there is no rigorous atlas applied, so the main points of interest are: WM, GM, VENTRICLE, CSF, and OUT, which represent white-matter, gray-matter, ventricle, cerebrospinal fluid and outside the brain. WM, Ventricle, CSF and OUT were removed channels from further analysis. These were labeled in the corresponding BIDS channels.tsv sidecar file as status=bad.

The dataset uploaded to openneuro.org does not contain the sourcedata since there was an extra anonymization step that occurred when fully converting to BIDS.

Derivatives

Derivatives include: * fragility analysis * frequency analysis * graph metrics analysis * figures

These can be computed by following the following paper: Neural Fragility as an EEG Marker for the Seizure Onset Zone

Channel locations in (x,y,z) coordinates

Unfortunately, the necessary T1 MRI, and CT scans to estimate these were not collected/processed, and exact channel locations are not available for any subject in this dataset as of 09/05/2023. The approximate brain regions of the hypothesized epileptic regions are stated in the metadata and paper.

Events and Descriptions

Within each EDF file, there contain event markers that are annotated by clinicians, which may inform you of specific clinical events that are occuring in time, or of when they saw seizures onset and offset (clinical and electrographic).

During a seizure event, specifically event markers may follow this time course:

* eeg onset, or clinical onset - the onset of a seizure that is either marked electrographically, or by clinical behavior. Note that the clinical onset may not always be present, since some seizures manifest without clinical behavioral changes.
* Marker/Mark On - these are usually annotations within some cases, where a health practitioner injects a chemical marker for use in ICTAL SPECT imaging after a seizure occurs. This is commonly done to see which portions of the brain are active metabolically.
* Marker/Mark Off - This is when the ICTAL SPECT stops imaging.
* eeg offset, or clinical offset - this is the offset of the seizure, as determined either electrographically, or by clinical symptoms.

Other events included may be beneficial for you to understand the time-course of each seizure. Note that ICTAL SPECT occurs in all Cleveland Clinic data. Note that seizure markers are not consistent in their description naming, so one might encode some specific regular-expression rules to consistently capture seizure onset/offset markers across all dataset. In the case of UMMC data, all onset and offset markers were provided by the clinicians on an Excel sheet instead of via the EDF file. So we went in and added the annotations manually to each EDF file.

Seizure Electrographic and Clinical Onset Annotations

For various datasets, there are seizures present within the dataset. Generally there is only one seizure per EDF file. When seizures are present, they are marked electrographically (and clinically if present) via standard approaches in the epilepsy clinical workflow.

Clinical onset are just manifestation of the seizures with clinical syndromes. Sometimes the maker may not be present.

Seizure Onset Zone Annotations

What is actually important in the evaluation of datasets is the clinical annotations of their localization hypotheses of the seizure onset zone.

These generally include:

* early onset: the earliest onset electrodes participating in the seizure that clinicians saw
* early/late spread (optional): the electrodes that showed epileptic spread activity after seizure onset. Not all seizures has spread contacts annotated.

Surgical Zone (Resection or Ablation) Annotations

For patients with the post-surgical MRI available, then the segmentation process outlined above tells us which electrodes were within the surgical removed brain region.

Otherwise, clinicians give us their best estimate, of which electrodes were resected/ablated based on their surgical notes.

For surgical patients whose postoperative medical records did not explicitly indicate specific resected or ablated contacts, manual visual inspection was performed to determine the approximate contacts that were located in later resected/ablated tissue. Postoperative T1 MRI scans were compared against post-SEEG implantation CT scans or CURRY coregistrations of preoperative MRI/post SEEG CT scans. Contacts of interest in and around the area of the reported resection were selected individually and the corresponding slice was navigated to on the CT scan or CURRY coregistration. After identifying landmarks of that slice (e.g. skull shape, skull features, shape of prominent brain structures like the ventricles, central sulcus, superior temporal gyrus, etc.), the location of a given contact in relation to these landmarks, and the location of the slice along the axial plane, the corresponding slice in the postoperative MRI scan was navigated to. The resected tissue within the slice was then visually inspected and compared against the distinct landmarks identified in the CT scans, if brain tissue was not present in the corresponding location of the contact, then the contact was marked as resected/ablated. This process was repeated for each contact of interest.

References

[1] Adam Li, Chester Huynh, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson, Nathan Crone, Sara Inati, Kareem Zaghloul, Juan Bulacio, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Neural Fragility as an EEG Marker of the Seizure Onset Zone. bioRxiv 862797; doi: https://doi.org/10.1101/862797

[2] Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

[3] Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

[4] Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

This dataset captures comprehensive multi-modal recordings from 10 expert and 10 novice drivers navigating 13 naturalistic urban driving conditions, synchronizing EEG brain activity with vehicle CAN bus data, traffic information, and psychophysiological measures (EDA and HR). Uniquely, it includes physiological responses and subjective feedback from two passengers per trip to validate driving performance quality, along with pre/post-experiment questionnaires and semi-structured interviews from all participants. The dataset enables researchers to decode neural signatures of driving expertise and understand the cognitive mechanisms underlying expert decision-making in complex urban environments. This resource provides crucial insights for developing more human-like autonomous driving algorithms by bridging the gap between human driving expertise and artificial intelligence. The multi-perspective approach combining driver neural data with passenger validation offers an unprecedented opportunity to understand what makes expert drivers excel in safety, comfort, and intelligent navigation.

The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.

The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.

Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.

In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.

NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering

If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Dataset ID: nm000232 Gifford2019

At a glance: EEG · 10 subjects · 638 recordings · CC-BY 4.0

  Load this dataset

This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.

pip install eegdash

from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="nm000232"… See the full description on the dataset page: https://huggingface.co/datasets/EEGDash/nm000232.

We present a mobile dataset of electroencephalography (EEG) from scalp and ear and locomotion sensors collected from 18 subjects moving at different speeds while performing brain-computer interface (BCI) tasks. The experiments were performed under 16 different conditions (2 types of EEG devices x 4 speeds of movements x 2 types of BCI paradigms). The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 3 inertial measurement units at the forehead, left ankle, and right ankle simultaneously. The conditions of recording were standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/sec, respectively. At each speed, two different BCI paradigms, event-related potential (ERP) and steady-state visual evoked potential (SSVEP), were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated at each speed. We expect that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and to evaluate the performance quantitatively, so as to broaden the use of practical BCIs.

The BED dataset

Version 1.0.0

Please cite as: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.

Disclaimer

While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.

Contact

For inquiries regarding the BED dataset, please contact:

1. Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com
2. Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk
3. Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es
4. Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk

Dataset summary

BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions.

The stimuli include:

• Images selected to elicit specific emotions
• Mathematical computations (2-digit additions)
• Resting-state with eyes closed
• Resting-state with eyes open
• Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal
• Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black

For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review)

Dataset structure and contents

The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other.

The BED dataset includes:

• The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format
• The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format
• The features extracted from each EEG segment, as described in the associated publication

The dataset is organised in 3 folders:

• RAW
• RAW_PARSED
• Features

RAW/ Contains the RAW files
RAW/sN/ Contains the RAW files associated with subject N
Each folder sN is composed by the following files:
- sN_s1.csv, sN_s2.csv, sN_s3.csv -- Files containing the EEG recordings for subject N and session 1, 2, and 3, respectively. These files contain 39 columns:
COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP
- subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively.

RAW_PARSED/
Contains Matlab files named sN_sM.mat. The files contain the recordings for the subject N in the session M. These files are composed by two variables:
- recording: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP
- events: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO
START_UNIX is the UNIX timestamp in which the event starts
END_UNIX is the UNIX timestamp in which the event ends
ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc..

Features/
Features/Identification
Features/Identification/[ARRC|MFCC|SPEC]/: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix.
- feat: N x number of features
- Y: N x 2 (the #subject and the #session)
- INFO: Contains details about the event same as the ADDITIONAL INFO
Features/Verification: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by:
- data: the time-series features, as described in the paper
- y: the #subject
- stimuli: the stimuli by name
- session: the #session
- INFO: Contains details about the event

The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus.

Additional information

For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.