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).

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).

**Overview:

The Bonn EEG Dataset is a widely recognized dataset in the field of biomedical signal processing and machine learning, specifically designed for research in epilepsy detection and EEG signal analysis. It contains electroencephalogram (EEG) recordings from both healthy individuals and patients with epilepsy, making it suitable for tasks such as seizure detection and classification of brain activity states. The dataset is structured into five distinct subsets (labeled A, B, C, D, and E), each comprising 100 single-channel EEG segments, resulting in a total of 500 segments. Each segment represents 23.6 seconds of EEG data, sampled at a frequency of 173.61 Hz, yielding 4,096 data points per segment, stored in ASCII format as text files.

****Structure and Label:

• Set A: EEG recordings from healthy individuals with eyes open, capturing normal brain activity under visual stimulation.
• Set B: EEG recordings from healthy individuals with eyes closed, reflecting brain activity in a resting state.
• Set C: EEG recordings from epilepsy patients, collected from the epileptogenic zone during an interictal (seizure-free) period.
• Set D: EEG recordings from epilepsy patients, collected from the hippocampal formation of the opposite brain hemisphere during an interictal period.
• Set E: EEG recordings from epilepsy patients during an ictal (seizure) period, capturing brain activity during an epileptic seizure. Each subset contains 100 EEG segments, ensuring a balanced distribution across the five classes, which supports both binary (e.g., healthy vs. epileptic) and multi-class (e.g., A-E classification) tasks.

**Key Characteristics

• Size: 500 EEG segments (100 segments per subset, across five subsets).
• Data Type: Single-channel EEG signals, stored in text files (ASCII format).
• Sampling Rate: 173.61 Hz, providing high temporal resolution.
• Segment Length: 23.6 seconds per segment, equivalent to 4,096 data points.
• Labels: Clearly defined for each subset (A: healthy, eyes open; B: healthy, eyes closed; C: interictal, epileptogenic zone; D: interictal, opposite hemisphere; E: ictal), enabling precise model evaluation.
• Preprocessing: The data is not pre-filtered, but a low-pass filter with a 40 Hz cutoff is recommended to remove high-frequency noise and artifacts, as suggested in the original documentation.

**Applications

The Bonn EEG Dataset is ideal for machine learning and signal processing tasks, including: - Developing algorithms for epileptic seizure detection and prediction. - Exploring feature extraction techniques, such as wavelet transforms, for EEG signal analysis. - Classifying brain states (healthy vs. epileptic, interictal vs. ictal). - Supporting research in neuroscience and medical diagnostics, particularly for epilepsy monitoring and treatment.

**Source

• The dataset is publicly available from the University of Bonn and can be downloaded from the following link: University of Bonn EEG Dataset
• The dataset is provided as five ZIP files, each containing 100 text files corresponding to the EEG segments for subsets A, B, C, D, and E.

**Citation

When using this dataset, researchers are required to cite the original publication: Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907. DOI: 10.1103/PhysRevE.64.061907.

**Additional Notes

1. The dataset is randomized, with no specific information provided about patients or electrode placements, ensuring simplicity and focus on signal characteristics.

2. The data is not hosted on Kaggle or Hugging Face but is accessible directly from the University of Bonn’s repository or mirrored sources.

3. Researchers may need to apply preprocessing steps, such as filtering or normalization, to optimize the data for machine learning tasks.

4. The dataset’s balanced structure and clear labels make it an excellent choice for a one-week machine learning project, particularly for tasks involving traditional algorithms like SVM, Random Forest, or Logistic Regression.

5. This dataset provides a robust foundation for learning signal processing, feature extraction, and machine learning techniques while addressing a real-world medical challenge in epilepsy detection.

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.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Description

We collected EEG signal data from 4 drivers while they were awake and asleep using NeuroSky MindWave sensor. For safety precautions they weren't actually driving while acquiring the signals. Each driver wore the helmet for 5-8 minutes for each label (sleepy, not sleepy) and the signals are acquired approximately every second. The signals are measured in units of microvolts squared per hertz (μV²/Hz). This is a measure of the power of the EEG signal at a particular frequency.

The high values that you are seeing are likely due to the fact that the MindWave sensor is only measuring EEG data from a single location on the forehead. This is in contrast to medical-grade EEG devices, which typically use multiple electrodes placed on different parts of the scalp.

Methodology

The driver would wear the NeuroSky MindWave headset connected by a USB stick to the laptop and we would collect EEG signals from their brain. The NeuroSky mindwave headset is a single channel headset that measures the voltage between an electrode resting on the frontal lobe (forehead) and two electrodes (one ground and one reference) each in contact with one earlobe. The drivers were instructed to be awake or asleep and their EEG signals were recorded accordingly.

Content

• Attention and meditation are calculated from the headset itself, we didn't consider it a reliable feature.
• All EEG signals are divided and specified by the headset. There was no signal preprocessing done. # Inspiration
• This dataset was made for our graduation project. We got an A- (90%).
• Our highest accuracy was 82%, hopefully you can do even better.
• I thought about uploading this dataset since we worked hard on it and it's a waste seeing it idle after we got our grade so hopefully other people might find it useful too! # More references
• here is our proposal document. The similar systems section would be very useful
• you will find some results at the end of the document they were used using this dataset as it used the same helmet as ours although not the same classification and that's why we got poor results but it was for a start

Data set from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-msec epoch) for 1 second. There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2. There were 122 subjects and each subject completed 120 trials where different stimuli were shown. The electrode positions were located at standard sites (Standard Electrode Position Nomenclature, American Electroencephalographic Association 1990). Zhang et al. (1995) describes in detail the data collection process. There are three versions of the EEG data set. * The Small Data Set (smni97_eeg_data.tar.gz) contains data for the 2 subjects, alcoholic a_co2a0000364 and control c_co2c0000337. For each of the 3 matching paradigms, c_1 (one presentation only), c_m (match to previous presentation) and c_n (no-match to previous presentation), 10 runs are shown. * The Large Data Set (SMNI_CMI_TRAIN.tar.gz and SMNI_CMI_TEST.tar.gz) contains data for 10 alcoholic and 10 control subjects, with 10 runs per subject per paradigm. The test data used the same 10 alcoholic and 10 control subjects as with the training data, but with 10 out-of-sample runs per subject per paradigm. * The Full Data Set contains all 120 trials for 122 subjects. The entire set of data is about 700 MBytes.

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.

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. With increasing open-source, raw, and large EEG datasets, there is a need for data standardization of patient and seizure-sensitive AI analysis with reduced redundant information. This work releases a balanced, annotated, fixed time and length meta-data of CHB-MIT Scalp EEG database v1.0.0.0.

The work releases patient-specific (inter and intra) and patient non-specific EEG data extracted using specific time stamps of ictal, pre-ictal, post-ictal, peri-ictal, and non-seizure EEG provided in the original dataset (annotations). Further details of this metadata can be found in the provided csv file (CHB-MIT DB timestamp.csv). The released EEG data is available in csv format and class labels are provided in the last row of the csv files. Data of ch06, ch12, ch23, and ch24 in patient-specific and chb24_11 in patient non-specific have not been included. The importance of peri-ictal EEGs has been elucidated in Handa, P., & Goel, N. (2021). Peri‐ictal and non‐seizure EEG event detection using generated metadata. Expert Systems, e12929.

Latest update: The dataset has been moved to: Data of Meta-EEGs

Please only download and cite the latest version!

This is the raw EEG data for the study. Data is in text file format (rows: data points, columns: channels). Data was converted from original hexadecimal format. Labeling is as follows: "Lab" = Laboratory recording; "Natural" = Natural (outside) recordings; "Eyes Open" = Eyes Open Resting State Condition; "Eyes Closed" = Eyes Closed Resting State Condition; "Math" = PASAT task.

Dataset motivation and summaryThe human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions coming from the THINGS database. We release this dataset as a tool to foster research in visual neuroscience and computer vision.Useful materialAdditional dataset informationFor information regarding the experimental paradigm, the EEG recording protocol and the dataset validation through computational modeling analyses please refer to our paper.Additional dataset resourcesPlease visit the dataset page for the paper, dataset tutorial, code and more.OSFFor additional data and resources visit our OSF project, where you can find:A detailed description of the raw EEG data filesThe preprocessed EEG dataThe stimuli imagesThe EEG resting state dataCitationsIf you use any of our data, please cite our paper.

Compiled the original 2010 database from Physionet.org

This dataset only includes the waveforms from the original dataset that have seizure events. Seizure events are annotated in seizure_events.csv in seconds.

Note: the waveforms are taken in 256Hz, the event onset and offset times are denoted in seconds

The original dataset 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. Recordings, grouped into 23 cases, were collected from 22 subjects (5 males, ages 3–22; and 17 females, ages 1.5–19). (Case chb21 was obtained 1.5 years after case chb01, from the same female subject.) The file SUBJECT-INFO contains the gender and age of each subject. (Case chb24 was added to this collection in December 2010, and is not currently included in SUBJECT-INFO.)

Each case (chb01, chb02, etc.) contains between 9 and 42 continuous .edf files from a single subject. Hardware limitations resulted in gaps between consecutively-numbered .edf files, during which the signals were not recorded; in most cases, the gaps are 10 seconds or less, but occasionally there are much longer gaps. In order to protect the privacy of the subjects, all protected health information (PHI) in the original .edf files has been replaced with surrogate information in the files provided here. Dates in the original .edf files have been replaced by surrogate dates, but the time relationships between the individual files belonging to each case have been preserved. In most cases, the .edf files contain exactly one hour of digitized EEG signals, although those belonging to case chb10 are two hours long, and those belonging to cases chb04, chb06, chb07, chb09, and chb23 are four hours long; occasionally, files in which seizures are recorded are shorter.

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). The International 10-20 system of EEG electrode positions and nomenclature was used for these recordings. In a few records, other signals are also recorded, such as an ECG signal in the last 36 files belonging to case chb04 and a vagal nerve stimulus (VNS) signal in the last 18 files belonging to case chb09. In some cases, up to 5 “dummy” signals (named "-") were interspersed among the EEG signals to obtain an easy-to-read display format; these dummy signals can be ignored.

The file RECORDS contains a list of all 664 .edf files included in this collection, and the file RECORDS-WITH-SEIZURES lists the 129 of those files that contain one or more seizures. In all, these records include 198 seizures (182 in the original set of 23 cases); the beginning ([) and end (]) of each seizure is annotated in the .seizure annotation files that accompany each of the files listed in RECORDS-WITH-SEIZURES. In addition, the files named chbnn-summary.txt contain information about the montage used for each recording, and the elapsed time in seconds from the beginning of each .edf file to the beginning and end of each seizure contained in it.

This database is described in:

Ali Shoeb. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of Technology, September 2009.

Please cite this publication when referencing this material, and also include the standard citation for PhysioNet:

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13)."

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).

Context : We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session. It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms. Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France. Duration : Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline. Documents Instructions: checklist read by experimenters during the experiments. Questionnaires: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version Performance: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires Scenarios/scripts : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI Database : raw signals Dataset A : N=60 participants Dataset B : N=21 participants Dataset C : N=6 participants

This is the Dataset Collected by Shahed Univeristy Released in IEEE.

the Columns are: Fz, Cz, Pz, C3, T3, C4, T4, Fp1, Fp2, F3, F4, F7, F8, P3, P4, T5, T6, O1, O2, Class, ID

the first 19 are channel names.

Class: ADHD/Control

ID: Patient ID

Participants were 61 children with ADHD and 60 healthy controls (boys and girls, ages 7-12). The ADHD children were diagnosed by an experienced psychiatrist to DSM-IV criteria, and have taken Ritalin for up to 6 months. None of the children in the control group had a history of psychiatric disorders, epilepsy, or any report of high-risk behaviors.

EEG recording was performed based on 10-20 standard by 19 channels (Fz, Cz, Pz, C3, T3, C4, T4, Fp1, Fp2, F3, F4, F7, F8, P3, P4, T5, T6, O1, O2) at 128 Hz sampling frequency. The A1 and A2 electrodes were the references located on earlobes.

Since one of the deficits in ADHD children is visual attention, the EEG recording protocol was based on visual attention tasks. In the task, a set of pictures of cartoon characters was shown to the children and they were asked to count the characters. The number of characters in each image was randomly selected between 5 and 16, and the size of the pictures was large enough to be easily visible and countable by children. To have a continuous stimulus during the signal recording, each image was displayed immediately and uninterrupted after the child’s response. Thus, the duration of EEG recording throughout this cognitive visual task was dependent on the child’s performance (i.e. response speed).

Citation Author(s): Ali Motie Nasrabadi Armin Allahverdy Mehdi Samavati Mohammad Reza Mohammadi

DOI: 10.21227/rzfh-zn36

License: Creative Commons Attribution

EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. Check https://www.youtube.com/watch?v=8lGBVvCX5d8&feature=youtu.be for a video demonstrating one trial.Check https://github.com/MAMEM/ssvep-eeg-processing-toolbox for the processing toolbox.Check http://arxiv.org/abs/1602.00904 for the technical report.

Tecnologico de Monterrey School of Engineering and Sciences NeuroTechs Research Group

Context: This dataset includes electroencephalographic (EEG) recordings from 34 healthy, young adults in Mexico, collected to study the somatosensory system's responses to a range of tactile stimuli. The study employs innovative NeuroSense tactile stimulators to explore how the brain processes touch sensations when subjected to stimuli such as air, vibration, and caress at four distinct intensity levels.

Objective: The objective of this database is to understand the cortical processing of tactile stimuli including air, vibration and carress using EEG.

Main Outcome Measure: The main outcome measure is the EEG recordings which includes the evoked responses of the somatosensory system to each type of stimulus and intensities. These measurements allow for an in-depth analysis of the cortical dynamics involved in processing touch.

Limitations: One limitation of the database is its focus on a relatively small and specific population, which could affect the generalizability of the findings. Additionally, the data is dependent on the accuracy and consistency of the stimulus delivery and EEG recording during the experimental sessions.

Generalizability: While the findings provide significant insights into the neural processing of tactile stimuli within the central nervous sytem, their generalizability might be limited due to the specialized nature of the stimuli and the controlled experimental conditions. However, the dataset serves as a valuable resource for developing diagnostic and therapeutic strategies for somatosensory impairments and advancing research in neuroscience and somatosensory rehabilitation.

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.

PCA

This zip file contains the raw EEG files for all 27 subjects and three experimental sessions. Pre-processing of the raw EEG data is fully automatic and can be reproduced with the analysis scripts.