100+ datasets found
  1. p

    CHB-MIT Scalp EEG Database

    • physionet.org
    Updated Jun 9, 2010
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    John Guttag (2010). CHB-MIT Scalp EEG Database [Dataset]. http://doi.org/10.13026/C2K01R
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    Dataset updated
    Jun 9, 2010
    Authors
    John Guttag
    License

    Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
    License information was derived automatically

    Description

    This database, collected at the Children’s Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. The recordings are grouped into 23 cases and were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19).

  2. i

    EEG Signal Dataset

    • ieee-dataport.org
    Updated Jun 11, 2020
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    Rahul Kher (2020). EEG Signal Dataset [Dataset]. https://ieee-dataport.org/documents/eeg-signal-dataset
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    Dataset updated
    Jun 11, 2020
    Authors
    Rahul Kher
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    PCA

  3. u

    EEG Datasets for Naturalistic Listening to "Alice in Wonderland" (Version 1)...

    • deepblue.lib.umich.edu
    Updated Nov 20, 2018
    + more versions
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    Brennan, Jonathan R. (2018). EEG Datasets for Naturalistic Listening to "Alice in Wonderland" (Version 1) [Dataset]. http://doi.org/10.7302/Z29C6VNH
    Explore at:
    Dataset updated
    Nov 20, 2018
    Dataset provided by
    Deep Blue Data
    Authors
    Brennan, Jonathan R.
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    These files contain the raw data and processing parameters to go with the paper "Hierarchical structure guides rapid linguistic predictions during naturalistic listening" by Jonathan R. Brennan and John T. Hale. These files include the stimulus (wav files), raw data (matlab format for the Fieldtrip toolbox), data processing paramters (matlab), and variables used to align the stimuli with the EEG data and for the statistical analyses reported in the paper.

  4. EEG Motor Imagery BCICIV_2a

    • kaggle.com
    Updated May 7, 2023
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    aymanmostafa11 (2023). EEG Motor Imagery BCICIV_2a [Dataset]. https://www.kaggle.com/datasets/aymanmostafa11/eeg-motor-imagery-bciciv-2a
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 7, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    aymanmostafa11
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    The dataset is a modified csv version of the BCI Competition IV 2a for ease of use for beginners

    Description

    The data can be interacted with two approaches: 1- Each patient separately: A csv file for each patient is provided for subject dependent tasks 2- All patients: the file with "all_patients" in it's name contain all patients data with a column specifying the patient number

    The events considered in the data are only the 4 target classes (left, right, foot, tongue), other events mentioned in the paper have been discarded for simplicity

    Acknowledgements

    BCI Competition IV This introductory youtube video

  5. h

    General-Disorders-EEG-Dataset-v1

    • huggingface.co
    Updated Jun 23, 2025
    + more versions
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    Neurazum (2025). General-Disorders-EEG-Dataset-v1 [Dataset]. http://doi.org/10.57967/hf/3321
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Neurazum
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset

    Synthetic EEG data generated by the ‘bai’ model based on real data.

      Features/Columns:
    

    No: "Number" Sex: "Gender" Age: "Age of participants" EEG Date: "The date of the EEG" Education: "Education level" IQ: "IQ level of participants" Main Disorder: "General class definition of the disorder" Specific Disorder: "Specific class definition of the disorder"

    Total Features/Columns: 1140

      Content:
    

    Obsessive Compulsive Disorder Bipolar Disorder Schizophrenia… See the full description on the dataset page: https://huggingface.co/datasets/Neurazum/General-Disorders-EEG-Dataset-v1.

  6. EEG datasets of stroke patients

    • figshare.com
    json
    Updated Sep 14, 2023
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    Haijie Liu; Xiaodong Lv (2023). EEG datasets of stroke patients [Dataset]. http://doi.org/10.6084/m9.figshare.21679035.v5
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 14, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Haijie Liu; Xiaodong Lv
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. The participants included 39 male and 11 female. The time after stroke ranged from 1 days to 30 days. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. All participants were originally right-handed. Each of the participants sat in front of a computer screen with an arm resting on a pillow on their lap or on a table and they carried out the instructions given on the computer screen. At the trial start, a picture with text description which was circulated with left right hand, were presented for 2s. We asked the participants to focus their mind on the hand motor imagery which was instructed, at the same time, the video of ipsilateral hand movement is displayed on the computer screen and lasts for 4s. Next, take a 2s break.

  7. b

    Harvard Electroencephalography Database

    • bdsp.io
    • registry.opendata.aws
    Updated Feb 10, 2025
    + more versions
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    Sahar Zafar; Tobias Loddenkemper; Jong Woo Lee; Andrew Cole; Daniel Goldenholz; Jurriaan Peters; Alice Lam; Edilberto Amorim; Catherine Chu; Sydney Cash; Valdery Moura Junior; Aditya Gupta; Manohar Ghanta; Marta Fernandes; Haoqi Sun; Jin Jing; M Brandon Westover (2025). Harvard Electroencephalography Database [Dataset]. http://doi.org/10.60508/k85b-fc87
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    Dataset updated
    Feb 10, 2025
    Authors
    Sahar Zafar; Tobias Loddenkemper; Jong Woo Lee; Andrew Cole; Daniel Goldenholz; Jurriaan Peters; Alice Lam; Edilberto Amorim; Catherine Chu; Sydney Cash; Valdery Moura Junior; Aditya Gupta; Manohar Ghanta; Marta Fernandes; Haoqi Sun; Jin Jing; M Brandon Westover
    License

    https://github.com/bdsp-core/bdsp-license-and-duahttps://github.com/bdsp-core/bdsp-license-and-dua

    Description

    The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University: Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH). The EEG data includes three types:

    rEEG: "routine EEGs" recorded in the outpatient setting.
    EMU: recordings obtained in the inpatient setting, within the Epilepsy Monitoring Unit (EMU).
    ICU/LTM: recordings obtained from acutely and critically ill patients within the intensive care unit (ICU).
    
  8. c

    Ultra high-density EEG recording of interictal migraine and controls:...

    • kilthub.cmu.edu
    txt
    Updated Jul 21, 2020
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    Alireza Chaman Zar; Sarah Haigh; Pulkit Grover; Marlene Behrmann (2020). Ultra high-density EEG recording of interictal migraine and controls: sensory and rest [Dataset]. http://doi.org/10.1184/R1/12636731
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    txtAvailable download formats
    Dataset updated
    Jul 21, 2020
    Dataset provided by
    Carnegie Mellon University
    Authors
    Alireza Chaman Zar; Sarah Haigh; Pulkit Grover; Marlene Behrmann
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We used a high-density electroencephalography (HD-EEG) system, with 128 customized electrode locations, to record from 17 individuals with migraine (12 female) in the interictal period, and 18 age- and gender-matched healthy control subjects, during visual (vertical grating pattern) and auditory (modulated tone) stimulation which varied in temporal frequency (4 and 6Hz), and during rest. This dataset includes the EEG raw data related to the paper entitled Chamanzar, Haigh, Grover, and Behrmann (2020), Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG. The link to our paper will be made available as soon as it is published online.

  9. i

    EEG datasets with different levels of fatigue for personal identification

    • ieee-dataport.org
    Updated May 2, 2023
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    Haixian Wang (2023). EEG datasets with different levels of fatigue for personal identification [Dataset]. https://ieee-dataport.org/documents/eeg-datasets-different-levels-fatigue-personal-identification
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    Dataset updated
    May 2, 2023
    Authors
    Haixian Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    the digital number represents different participants. The .cnt files were created by a 40-channel Neuroscan amplifier

  10. i

    Preprocessed CHB-MIT Scalp EEG Database

    • ieee-dataport.org
    Updated Jan 24, 2023
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    Mrs Deepa .B (2023). Preprocessed CHB-MIT Scalp EEG Database [Dataset]. https://ieee-dataport.org/open-access/preprocessed-chb-mit-scalp-eeg-database
    Explore at:
    Dataset updated
    Jan 24, 2023
    Authors
    Mrs Deepa .B
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Univ. of Bonn’ and ‘CHB-MIT Scalp EEG Database’ are publically available datasets which are the most sought after amongst researchers. Bonn dataset is very small compared to CHB-MIT. But still researchers prefer Bonn as it is in simple '.txt' format. The dataset being published here is a preprocessed form of CHB-MIT. The dataset is available in '.csv' format.

  11. EEG Alpha Waves dataset

    • zenodo.org
    • search.datacite.org
    bin
    Updated Jan 24, 2020
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    Grégoire Cattan; Pedro L. C. Rodrigues; Pedro L. C. Rodrigues; Marco Congedo; Marco Congedo; Grégoire Cattan (2020). EEG Alpha Waves dataset [Dataset]. http://doi.org/10.5281/zenodo.2348892
    Explore at:
    binAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Grégoire Cattan; Pedro L. C. Rodrigues; Pedro L. C. Rodrigues; Marco Congedo; Marco Congedo; Grégoire Cattan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary:

    This dataset contains electroencephalographic recordings of subjects in a simple resting-state eyes open/closed experimental protocol. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 [1]. Python code is available at https://github.com/plcrodrigues/Alpha-Waves-Dataset for manipulating the data.

    Principal Investigators: Eng. Grégoire CATTAN, Eng. Pedro L. C. RODRIGUES
    Scientific Supervisor: Dr. Marco Congedo

    Introduction :

    The occipital dominant rhythm (commonly referred to as occipital ‘Alpha’) is prominent in occipital and parietal regions when a subject is exempt of visual stimulations, as in the case when keeping the eyes closed (2). In normal subjects its peak frequency is in the range 8-12Hz. The detection of alpha waves on the ongoing electroencephalography (EEG) is a useful indicator of the subject’s level of stress, concentration, relaxation or mental load (3,4) and an easy marker to detect in the recorded signals because of its high signal-to-noise-ratio. This experiment was conducted to provide a simple yet reliable set of EEG signals carrying very distinct signatures on each experimental condition. It can be useful for researchers and students looking for an EEG dataset to perform tests with signal processing and machine learning algorithms. An example of application of this dataset can be seen in (5).

    I.Participants

    A total of 20 volunteers participated in the experiment (7 females), with mean (sd) age 25.8 (5.27) and median 25.5. 18 subjects were between 19 and 28 years old. Two participants with age 33 and 44 were outside this range.

    II.Procedures

    EEG signals were acquired using a standard research grade amplifier (g.USBamp, g.tec, Schiedlberg, Austria) and the EC20 cap equipped with 16 wet electrodes (EasyCap, Herrsching am Ammersee, Germany), placed according to the 10-20 international system. The locations of the electrodes were FP1, FP2, FC5, FC6, FZ, T7, CZ, T8, P7, P3, PZ, P4, P8, O1, Oz, and O2. The reference was placed on the right earlobe and the ground at the AFZ scalp location. The amplifier was linked by USB connection to the PC where the data were acquired by means of the software OpenVibe (6,7). We acquired the data with no digital filter and a sampling frequency of 512 samples per second was used. For ensuing analyses, the experimenter was able to tag the EEG signal using an in-house application based on a C/C++ library (8). The tag were sent by the application to the amplifier through the USB port of the PC. It was then recorded along with the EEG signal as a supplementary channel.

    For each recording we provide the age, genre and fatigue of each participant. Fatigue was evaluated by the subjects thanks to a scale ranging from 0 to 10, where 10 represents exhaustion. Each participant underwent one session consisting of ten blocks of ten seconds of EEG data recording. Five blocks were recorded while a subject was keeping his eyes closed (condition 1) and the others while his eyes were open (condition 2). The two conditions were alternated. Before the onset of each block, the subject was asked to close or open his eyes according to the experimental condition. The experimenter then tagged the EEG signal using the in-house application and started a 10-second countdown of a block.

    III.Organization of the dataset

    For each subject we provide a single .mat file containing the complete recording of the session. The file is a 2D-matrix where the rows contain the observations at each time sample. Columns 2 to 17 contain the recordings on each of the 16 EEG electrodes. The first column of the matrix represents the timestamp of each observation and column 18 and 19 contain the triggers for the experimental condition 1 and 2. The rows in column 18 (resp. 19) are filled with zeros, except at the timestamp corresponding to the beginning of the block for condition 1 (resp. 2), when the row gets a value of one.

    We supply an online and open-source example working with Python (9).

    IV.References

    1. Cattan G, Andreev A, Mendoza C, Congedo M. The Impact of Passive Head-Mounted Virtual Reality Devices on the Quality of EEG Signals. In Delft: The Eurographics Association; 2018 [cited 2018 Apr 16]. Available from: https://diglib.eg.org:443/handle/10.2312/vriphys20181064

    2. Pfurtscheller G, Stancák A, Neuper C. Event-related synchronization (ERS) in the alpha band — an electrophysiological correlate of cortical idling: A review. Int J Psychophysiol. 1996 Nov 1;24(1):39–46.

    3. Banquet JP. Spectral analysis of the EEG in meditation. Electroencephalogr Clin Neurophysiol. 1973 Aug 1;35(2):143–51.

    4. Antonenko P, Paas F, Grabner R, van Gog T. Using Electroencephalography to Measure Cognitive Load. Educ Psychol Rev. 2010 Dec 1;22(4):425–38.

    5. Rodrigues PLC, Congedo M, Jutten C. Multivariate Time-Series Analysis Via Manifold Learning. In: 2018 IEEE Statistical Signal Processing Workshop (SSP). 2018. p. 573–7.

    6. Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, et al. OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain–Computer Interfaces in Real and Virtual Environments. Presence Teleoperators Virtual Environ. 2010 Feb 1;19(1):35–53.

    7. Arrouët C, Congedo M, Marvie J-E, Lamarche F, Lécuyer A, Arnaldi B. Open-ViBE: A Three Dimensional Platform for Real-Time Neuroscience. J Neurother. 2005 Jul 8;9(1):3–25.

    8. Mandal MK. C++ Library for Serial Communication with Arduino [Internet]. 2016 [cited 2018 Dec 15]. Available from : https://github.com/manashmndl/SerialPort

    9. Rodrigues PLC. Alpha-Waves-Dataset [Internet]. Grenoble: GIPSA-lab; 2018. Available from : https://github.com/plcrodrigues/Alpha-Waves-Dataset

  12. f

    EEG Dataset for 'Immediate effects of short-term meditation on sensorimotor...

    • figshare.com
    pdf
    Updated Dec 9, 2022
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    Jeehyun Kim; Xiyuan Jiang; Dylan Forenzo; Bin He (2022). EEG Dataset for 'Immediate effects of short-term meditation on sensorimotor rhythm-based brain–computer interface performance' [Dataset]. http://doi.org/10.6084/m9.figshare.21644429.v5
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Dec 9, 2022
    Dataset provided by
    figshare
    Authors
    Jeehyun Kim; Xiyuan Jiang; Dylan Forenzo; Bin He
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This database includes the de-identified EEG data from 37 healthy individuals who participated in a brain-computer interface (BCI) study. All but one subject underwent 2 sessions of BCI experiments that involved controlling a computer cursor to move in one-dimensional space using their “intent”. EEG data were recorded with 62 electrodes. In addition to the EEG data, behavioral data including the online success rate and results of BCI cursor control are also included. This dataset was collected under support from the National Institutes of Health via grant AT009263 to Dr. Bin He. Correspondence about the dataset: Dr. Bin He, Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, PA 15213. E-mail: bhe1@andrew.cmu.edu This dataset has been used and analyzed to study the immediate effect of short meditation on BCI performance. The results are reported in: Kim et al, “Immediate effects of short-term meditation on sensorimotor rhythm-based brain–computer interface performance,” Frontiers in Human Neuroscience, 2022 (https://doi.org/10.3389/fnhum.2022.1019279). Please cite this paper if you use any data included in this dataset.

  13. i

    Data from: EEG data for ADHD / Control children

    • ieee-dataport.org
    Updated Oct 15, 2024
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    Ali Motie Nasrabadi (2024). EEG data for ADHD / Control children [Dataset]. https://ieee-dataport.org/open-access/eeg-data-adhd-control-children
    Explore at:
    Dataset updated
    Oct 15, 2024
    Authors
    Ali Motie Nasrabadi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    epilepsy

  14. R

    Data from: EEG in schizophrenia

    • repod.icm.edu.pl
    bin
    Updated Sep 1, 2017
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    Olejarczyk, Elzbieta; Jernajczyk, Wojciech (2017). EEG in schizophrenia [Dataset]. http://doi.org/10.18150/repod.0107441
    Explore at:
    bin(10331620), bin(11262620), bin(8460120), bin(8080120), bin(8555120), bin(7035120), bin(11452620), bin(8840120), bin(8792620), bin(9163120), bin(8222620), bin(8650120), bin(12792120), bin(9172620), bin(10597620), bin(10882620), bin(8602620), bin(8982620), bin(8032620), bin(8697620), bin(10787620), bin(8659620), bin(12925120), bin(20620120)Available download formats
    Dataset updated
    Sep 1, 2017
    Dataset provided by
    RepOD
    Authors
    Olejarczyk, Elzbieta; Jernajczyk, Wojciech
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The dataset comprised 14 patients with paranoid schizophrenia and 14 healthy controls. Data were acquired with the sampling frequency of 250 Hz using the standard 10-20 EEG montage with 19 EEG channels: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2. The reference electrode was placed between electrodes Fz and Cz.

  15. f

    Human EEG Dataset for Brain-Computer Interface and Meditation

    • figshare.com
    pdf
    Updated May 30, 2023
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    James Stieger (2023). Human EEG Dataset for Brain-Computer Interface and Meditation [Dataset]. http://doi.org/10.6084/m9.figshare.13123148.v1
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    figshare
    Authors
    James Stieger
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This database includes the de-identified EEG data from 62 healthy individuals who participated in a brain-computer interface (BCI) study. All subjects underwent 7-11 sessions of BCI training which involves controlling a computer cursor to move in one-dimensional and two-dimensional spaces using subject’s “intent”. EEG data were recorded with 62 electrodes. In addition to the EEG data, behavioral data including the online success rate of BCI cursor control are also included.This dataset was collected under support from the National Institutes of Health via grants AT009263, EB021027, NS096761, MH114233, RF1MH to Dr. Bin He. Correspondence about the dataset: Dr. Bin He, Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, PA 15213. E-mail: bhe1@andrew.cmu.edu This dataset has been used and analyzed to study the learning of BCI control and the effects of mind-body awareness training on this process. The results are reported in: Stieger et al, “Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band,” Cerebral Cortex, 2020 (https://doi.org/10.1093/cercor/bhaa234). Please cite this paper if you use any data included in this dataset.

  16. f

    EEG dataset for the analysis of age-related changes in motor-related...

    • figshare.com
    png
    Updated Nov 19, 2020
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    Nikita Frolov; Elena Pitsik; Vadim V. Grubov; Anton R. Kiselev; Vladimir Maksimenko; Alexander E. Hramov (2020). EEG dataset for the analysis of age-related changes in motor-related cortical activity during a series of fine motor tasks performance [Dataset]. http://doi.org/10.6084/m9.figshare.12301181.v2
    Explore at:
    pngAvailable download formats
    Dataset updated
    Nov 19, 2020
    Dataset provided by
    figshare
    Authors
    Nikita Frolov; Elena Pitsik; Vadim V. Grubov; Anton R. Kiselev; Vladimir Maksimenko; Alexander E. Hramov
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    EEG signals were acquired from 20 healthy right-handed subjects performing a series of fine motor tasks cued by the audio command. The participants were divided equally into two distinct age groups: (i) 10 elderly adults (EA group, aged 55-72, 6 females); (ii) 10 young adults (YA group, aged 19-33, 3 females).The active phase of the experimental session included sequential execution of 60 fine motor tasks - squeezing a hand into a fist after the first audio command and holding it until the second audio command (30 repetitions per hand) (see Fig.1). Duration of the audio command determined type of the motor action to be executed: 0.25s for left hand (LH) movement and 0.75s for right rand (RH) movement. The time interval between two audio signals was selected randomly in the range 4-5s for each trial. The sequence of motor tasks was randomized and the pause between tasks was also chosen randomly in the range 6-8s to exclude possible training or motor-preparation effects caused by the sequential execution of the same tasks.Acquired EEG signals were then processed via preprocessing tools implemented in MNE Python package. Specifically, raw EEG signals were filtered by a Butterworth 5th order filter in the range 1-100 Hz, and by 50Hz Notch filter. Further, Independent Component Analysis (ICA) was applied to remove ocular and cardiac artifacts. Artifact-free EEG recordings were then segmented into 60 epochs according to the experimental protocol. Each epoch was 14s long, including 3s of baseline and 11s of motor-related brain activity, and time-locked to the first audio command indicating the start of motor execution. After visual inspection epochs that still contained artifacts were rejected. Finally, 15 epochs per movement type were stored for each subject.Individual epochs for each subject are stored in the attached MNE .fif files. Prefix EA or YA in the name of the file identifies the age group, which subject belongs to. Postfix LH or RH in the name of the file indicates the type of motor tasks.EEG signals were acquired from 20 healthy right-handed subjects performing a series of fine motor tasks cued by the audio command. The participants were divided equally into two distinct age groups: (i) 10 elderly adults (EA group, aged 55-72, 6 females); (ii) 10 young adults (YA group, aged 19-33, 3 females).

  17. EEG dataset

    • figshare.com
    bin
    Updated Dec 6, 2019
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    minho lee (2019). EEG dataset [Dataset]. http://doi.org/10.6084/m9.figshare.8091242.v1
    Explore at:
    binAvailable download formats
    Dataset updated
    Dec 6, 2019
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    minho lee
    License

    https://www.gnu.org/copyleft/gpl.htmlhttps://www.gnu.org/copyleft/gpl.html

    Description

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

  18. EEG Dataset for Emotion Classification Using Low-Cost and High-End Equipment...

    • zenodo.org
    Updated Feb 1, 2025
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    Roberto Sanchez-Reolid; Roberto Sanchez-Reolid (2025). EEG Dataset for Emotion Classification Using Low-Cost and High-End Equipment [Dataset]. http://doi.org/10.5281/zenodo.14787743
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    Dataset updated
    Feb 1, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Roberto Sanchez-Reolid; Roberto Sanchez-Reolid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Description:
    This dataset contains electroencephalography (EEG) signals recorded during an emotion classification experiment using two devices: a high-end professional EEG system (BrainVision) and a low-cost brain-computer interface (Emotiv EPOC+). Data were collected from 20 participants while they were exposed to visual stimuli from the International Affective Picture System (IAPS), designed to induce four emotional states based on Russell’s valence-arousal model:

    • HVHA: High valence, high arousal
    • HVLA: High valence, low arousal
    • LVHA: Low valence, high arousal
    • LVLA: Low valence, low arousal

    The dataset includes raw EEG recordings, preprocessed signals, and extracted features for further analysis. Additionally, a README file provides detailed information on the data structure, device configurations, and emotional labels assigned to each signal segment.

    Data Format:

    • Raw EEG data: Original signal recordings (.edf, .csv, .mat).
    • Preprocessed EEG: Filtered and normalized data (.csv, .mat).
    • Extracted features: Set of statistical, spectral, and entropy-based features used in the study (.csv, .xlsx).
    • Metadata: Participant information (anonymized), experimental details, and emotional condition labels (.txt, .json).

    Usage and Applications:
    This dataset can be used for research in neuroscience, emotion classification, artificial intelligence, machine learning, and EEG signal processing. It is particularly suitable for developing and validating machine learning and deep learning models applied to emotion recognition from brain signals.

    License & Accessibility:
    The dataset is publicly available under the Creative Commons Attribution (CC BY 4.0) license, allowing free use, distribution, and modification with proper attribution.

    Recommended Citation:
    If you use this dataset in your research, please cite the associated publication:

    Sánchez-Reolid, R., Martínez-Sáez, M. C., García-Martínez, B., Fernández-Aguilar, L., Ros, L., Latorre, J. M., & Fernández-Caballero, A. (2022). Emotion classification from EEG with a low-cost BCI versus a high-end equipment. International Journal of Neural Systems, 32(10), 2250041. World Scientific. https://doi.org/10.1142/S0129065722500411

  19. o

    THINGS-EEG: Human electroencephalography recordings from 50 subjects for...

    • osf.io
    Updated Jan 17, 2022
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    Tijl Grootswagers; Ivy Zhou; Amanda Robinson; Martin Hebart; Thomas Carlson (2022). THINGS-EEG: Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts [Dataset]. http://doi.org/10.17605/OSF.IO/HD6ZK
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    Dataset updated
    Jan 17, 2022
    Dataset provided by
    Center For Open Science
    Authors
    Tijl Grootswagers; Ivy Zhou; Amanda Robinson; Martin Hebart; Thomas Carlson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

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

  20. SRM Resting-state EEG

    • openneuro.org
    Updated Nov 23, 2022
    + more versions
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    Christoffer Hatlestad-Hall; Trine Waage Rygvold; Stein Andersson (2022). SRM Resting-state EEG [Dataset]. http://doi.org/10.18112/openneuro.ds003775.v1.2.1
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    Dataset updated
    Nov 23, 2022
    Dataset provided by
    OpenNeurohttps://openneuro.org/
    Authors
    Christoffer Hatlestad-Hall; Trine Waage Rygvold; Stein Andersson
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    SRM Resting-state EEG

    Introduction

    This EEG dataset contains resting-state EEG extracted from the experimental paradigm used in the Stimulus-Selective Response Modulation (SRM) project at the Dept. of Psychology, University of Oslo, Norway.

    The data is recorded with a BioSemi ActiveTwo system, using 64 electrodes following the positional scheme of the extended 10-20 system (10-10). Each datafile comprises four minutes of uninterrupted EEG acquired while the subjects were resting with their eyes closed. The dataset includes EEG from 111 healthy control subjects (the "t1" session), of which a number underwent an additional EEG recording at a later date (the "t2" session). Thus, some subjects have one associated EEG file, whereas others have two.

    Disclaimer

    The dataset is provided "as is". Hereunder, the authors take no responsibility with regard to data quality. The user is solely responsible for ascertaining that the data used for publications or in other contexts fulfil the required quality criteria.

    The data

    Raw data files

    The raw EEG data signals are rereferenced to the average reference. Other than that, no operations have been performed on the data. The files contain no events; the whole continuous segment is resting-state data. The data signals are unfiltered (recorded in Europe, the line noise frequency is 50 Hz). The time points for the subject's EEG recording(s), are listed in the *_scans.tsv file (particularly interesting for the subjects with two recordings).

    Please note that the quality of the raw data has not been carefully assessed. While most data files are of high quality, a few might be of poorer quality. The data files are provided "as is", and it is the user's esponsibility to ascertain the quality of the individual data file.

    /derivatives/cleaned_data

    For convenience, a cleaned dataset is provided. The files in this derived dataset have been preprocessed with a basic, fully automated pipeline (see /code/s2_preprocess.m for details) directory for details. The derived files are stored as EEGLAB .set files in a directory structure identical to that of the raw files. Please note that the *_channels.tsv files associated with the derived files have been updated with status information about each channel ("good" or "bad"). The "bad" channels are – for the sake of consistency – interpolated, and thus still present in the data. It might be advisable to remove these channels in some analyses, as they (per definition) do not provide anything to the EEG data. The cleaned data signals are referenced to the average reference (including the interpolated channels).

    Please mind the automatic nature of the employed pipeline. It might not perform optimally on all data files (e.g. over-/underestimating proportion of bad channels). For publications, we recommend implementing a more sensitive cleaning pipeline.

    Demographic and cognitive test data

    The participants.tsv file in the root folder contains the variables age, sex, and a range of cognitive test scores. See the sidecar participants.json for more information on the behavioural measures. Please note that these measures were collected in connection with the "t1" session recording.

    How to cite

    All use of this dataset in a publication context requires the following paper to be cited:

    Hatlestad-Hall, C., Rygvold, T. W., & Andersson, S. (2022). BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm. Data in Brief, 45, 108647. https://doi.org/10.1016/j.dib.2022.108647

    Contact

    Questions regarding the EEG data may be addressed to Christoffer Hatlestad-Hall (chr.hh@pm.me).

    Question regarding the project in general may be addressed to Stein Andersson (stein.andersson@psykologi.uio.no) or Trine W. Rygvold (t.w.rygvold@psykologi.uio.no).

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John Guttag (2010). CHB-MIT Scalp EEG Database [Dataset]. http://doi.org/10.13026/C2K01R

CHB-MIT Scalp EEG Database

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306 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 9, 2010
Authors
John Guttag
License

Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
License information was derived automatically

Description

This database, collected at the Children’s Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention. The recordings are grouped into 23 cases and were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19).

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