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).

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 (BrainVision format), data processing parameters (matlab), and variables used to align the stimuli with the EEG data and for the statistical analyses reported in the paper (csv spreadsheet). ;Updates in Version 2:

• data in BrainVision format
• added information about data analysis
• corrected prePROCessing information for S02

Source, raw and preprocessed EEG data, resting state EEG data, image set, DNN feature maps and code of the paper: "A large and rich EEG dataset for modeling human visual object recognition".

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

This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:

• Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. NeuroImage, 156, 435-444

and

• Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, https://doi.org/10.3389/fnins.2018.00531

The data is organized in format of the publicly available COCOHA Matlab Toolbox. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in data.eeg with the following channels:

• channels 1-64: scalp EEG electrodes
• channel 65: right mastoid electrode
• channel 66: left mastoid electrode
• channel 67: vertical EOG below right eye
• channel 68: horizontal EOG right eye
• channel 69: vertical EOG above right eye
• channel 70: vertical EOG below left eye
• channel 71: horizontal EOG left eye
• channel 72: vertical EOG above left eye

The expinfo table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:

• attend_mf: attended speaker (1=male, 2=female)
• attend_lr: spatial position of the attended speaker (1=left, 2=right)
• acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)
• n_speakers: number of speakers presented (1 or 2)
• wavfile_male: name of presented audio wav-file for the male speaker
• wavfile_female: name of presented audio wav-file for the female speaker (if any)
• trigger: trigger event value for each trial also found in data.event.eeg.value

DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.

The dataset was created within the COCOHA Project: Cognitive Control of a Hearing Aid

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.

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.

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).

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.

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

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.

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

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.

epilepsy

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.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on November 22, 2022. Data set collected at the Children''s Hospital Boston, 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. All signals were sampled at 256 samples per second with 16-bit resolution. Most files contain 23 EEG signals (24 or 26 in a few cases).

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.

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).

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