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).

in-home environments.

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).

A comprehensive database for human surface and intracranial EEG data that is suitable for a broad range of applications e.g. of time series analyses of brain activity. Currently, the EU database contains annotated EEG datasets from more than 200 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of at least 96 hours (4 days) at a sample rate of up to 2500 Hz. Clinical patient information and MR imaging data supplement the EEG data. The total duration of EEG recordings included execeeds 30000 hours. The database is composed of different modalities: Binary files with EEG recording / MR imaging data and Relational database for supplementary meta data.

The Imaging Database for Epilepsy And Surgery (IDEAS)

Peter N. Taylor, Yujiang Wang, Callum Simpson, Vytene Janiukstyte, Jonathan Horsley, Karoline Leiberg, Beth Little, Harry Clifford, Sophie Adler, Sjoerd B. Vos, Gavin P Winston, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, John S Duncan

Magnetic resonance imaging (MRI) is a crucial tool to identify brain abnormalities in a wide range of neurological disorders. In focal epilepsy MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Herein, we release an open-source dataset of preprocessed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections, and detailed demographic information. The MRI scan data includes the preoperative 3D T1 and where available 3D FLAIR, as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age of onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical follow up. Crucially, we also include resection masks delineated from post-surgical imaging. To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of around 50%. Our imaging data replicates findings of group level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes. We envisage our dataset, shared openly with the community, will catalyse the development and application of computational methods in clinical neurology.

https://arxiv.org/abs/2406.06731

This release on OpenNeuro includes only raw T1w and FLAR scans. Fully processed data, including resection masks and other demographic information can be found at the following locations: https://www.cnnp-lab.com/ideas-data

• Bids https://figshare.com/s/07fca72410094bc49506 Raw T1w and FLAIR scans organised in BIDS format. Nifti and json descriptors included

• Masks https://figshare.com/s/31ab43d1829b12ac13e8 Resection masks for IDEAS cohort in native, and freesurfer orig.mgz space

• Freesurfer_brain https://figshare.com/s/39b61a1df5fa8443e3c4 skullstripped brain from freesurfer in nifti format

• Freesurfer_orig https://figshare.com/s/f13391a4161b807ce6b0 freesurfer orig.mgz converted to nifti format

• Freesurfer_zip https://figshare.com/s/b13b8bb41390d3f7a088 freesurfer surface and volumetric reconstructions

• Tables_stats_freesurfer https://figshare.com/s/010142dd51e37ba4e4e2 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation.

• Tables_metadata https://figshare.com/s/bab70268afeb1071202b clinical and demographic metadata

• Table_resected https://figshare.com/s/097ba0e254e36f0eee52 table indicating the percentage of each brain region in the Desikan-Kiliany atlas subsequently resected by surgery.

• Tables_zscores https://figshare.com/s/8c086fc295a75f85e628 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation, z-scored against normative controls post-combat.

• Tables_group_effect https://figshare.com/s/323db205354788c4d1f0 Group effect size differences to controls

The Epilepsy Genetic Association Database (epiGAD) is an online repository of data relating to genetic association studies in the field of epilepsy. It summarizes the results of both published and unpublished studies, and is intended as a tool for researchers in the field to keep abreast of recent studies, providing a bird''s eye view of this research area. The goal of epiGAD is to collate all association studies in epilepsy in order to help researchers in this area identify all the available gene-disease associations. Finally, by including unpublished studies, it hopes to reduce the problem of publication bias and provide more accurate data for future meta-analyses. It is also hoped that epiGAD will foster collaboration between the different epilepsy genetics groups around the world, and faciliate formation of a network of investigators in epilepsy genetics. There are 4 databases within epiGAD: - the susceptibility genes database - the epilepsy pharmacogenetics database - the meta-analysis database - the genome-wide association studies (GWAS) database The susceptibility genes database compiles all studies related to putative epilepsy susceptibility genes (eg. interleukin-1-beta in TLE), while the pharmacogenetics studies in epilepsy (eg. ABCB1 studies) are stored in ''phamacogenetics''. The meta-analysis database compiles all existing published epilepsy genetic meta-analyses, whether for susceptibility genes, or pharmacogenetics. The GWAS database is currently empty, but will be filled once GWAS are published. Sponsors: The epiGAD website is supported by the ILAE Genetics Commission.

We present an MRI dataset of 85 people with epilepsy due to focal cortical dysplasia (FCD) type II and 85 healthy controls. In epilepsy imaging, automated detection of FCDs plays a vital role because FCDs often escape conventional MRI analysis. Accurate recognition of FCDs is essential for affected patients. Surgical resection of the dysplastic cortex is associated with a high success rate, and a substantial number of patients subsequently become seizure-free. We hope this dataset's publication will improve computer-aided FCD detection by enabling the validation of existing algorithms or aiding the development of new approaches. Our dataset includes MRI data from T1 and FLAIR weighted images, manually labeled lesion masks, and selected clinical features.

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.

HUP iEEG dataset

This dataset was prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in Kini & Bernabei et al., Brain (2019) [1], and Bernabei & Sinha et al., Brain (2022) [2].

Dataset description

These files contain de-identified patient data collected as part of surgical treatment for drug resistant epilepsy at the Hospital of the University of Pennsylvania. Each of the 58 subjects underwent intracranial EEG with subdural grid, strip, and depth electrodes (ECoG) or purely stereotactically-placed depth electrodes (SEEG). Each patient also underwent subsequent treatment with surgical resection or laser ablation. Electrophysiologic data for both interictal and ictal periods is available, as are electrode localizations in ICBM152 MNI space. Furthermore, clinically-determined seizure onset channels are provided, as are channels which overlap with the resection/ablation zone, which was rigorously determined by segmenting the resection cavity.

BIDS Conversion

MNE-BIDS was used to convert the dataset into BIDS format.

References

[1] Kini L.*, Bernabei J.M.*, Mikhail F., Hadar P., Shah P., Khambhati A., Oechsel K., Archer R., Boccanfuso J.A., Conrad E., Stein J., Das S., Kheder A., Lucas T.H., Davis K.A., Bassett D.S., Litt B., Virtual resection predicts surgical outcome for drug resistant epilepsy. Brain, 2019.

[2] Bernabei J.M.*, Sinha N.*, Arnold T.C., Conrad E., Ong I., Pattnaik A.R., Stein J.M., Shinohara R.T., Lucas T.H., Bassett D.S., Davis K.A., Litt B., Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain, 2022

[3] 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

[4] 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

Epilepsy related database search.

The original dataset from the reference consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time. So we have total 500 individuals with each has 4097 data points for 23.5 seconds.

We divided and shuffled every 4097 data points into 23 chunks, each chunk contains 178 data points for 1 second, and each data point is the value of the EEG recording at a different point in time. So now we have 23 x 500 = 11500 pieces of information(row), each information contains 178 data points for 1 second(column), the last column represents the label y {1,2,3,4,5}.

The response variable is y in column 179, the Explanatory variables X1, X2, ..., X178

y contains the category of the 178-dimensional input vector. Specifically y in {1, 2, 3, 4, 5}:

5 - eyes open, means when they were recording the EEG signal of the brain the patient had their eyes open

4 - eyes closed, means when they were recording the EEG signal the patient had their eyes closed

3 - Yes they identify where the region of the tumor was in the brain and recording the EEG activity from the healthy brain area

2 - They recorder the EEG from the area where the tumor was located

1 - Recording of seizure activity

All subjects falling in classes 2, 3, 4, and 5 are subjects who did not have epileptic seizure. Only subjects in class 1 have epileptic seizure. Our motivation for creating this version of the data was to simplify access to the data via the creation of a .csv version of it. Although there are 5 classes most authors have done binary classification, namely class 1 (Epileptic seizure) against the rest.

This page provides the data of the manuscript: Martínez, C. G. B., Niediek, J., Mormann, F. & Andrzejak,R. G. Seizure onset zone lateralization using a nonlinear analysis of micro versus macro electroencephalographic recordings during seizure-free stages of the sleep-wake cycle from epilepsy patients. Frontiers in Neurology 11, 1057, 2020. If you use any of this data, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

This database includes the de-identified interictal spike information in 9 focal epilepsy patients who became seizure-free after surgery. All underwent intra-cranial EEG (iEEG) monitoring prior to surgery and information about seizure onset zone (SOZ) based on iEEG study was available in these patients.

This dataset was collected under support from the National Institutes of Health via grants R01NS096761 and R01EB021027, to Dr. Bin He and Dr. Greg Worrell. The human data were collected and deidentified as part of NIH funded research at Mayo Clinic, Rochester, overseen by Dr. Greg Worrell, and processed and organized at Dr. Bin He’s lab at Carnegie Mellon University.

This dataset has been used and analyzed to study epilepsy networks and the results are reported in: Sohrabpour et al, “Noninvasive Electromagnetic Source Imaging of Spatio-temporally Distributed Epileptogenic Brain Sources,” Nature Communications, 2020 (https://doi.org/10.1038/s41467-020-15781-0). Please cite this paper if you use any data included in this dataset. Codes related to this study are also available from https://github.com/bfinl/FAST-IRES.

The Human Epilepsy Project (HEP) is a multi-institutional series of prospective, observational studies to identify factors that predict disease outcome, progression, and treatment response among participants with epilepsy.

• HEP, conducted between 2012 and 2020, included 488 participants aged 12 to 60 years old with new onset focal epilepsy recruited in Australia, Canada, and the United States. Participants were followed for 5 years and contributed clinical measures, blood samples, magnetic resonance imaging (MRI) data, electroencephalograms (EEG), and seizure diary data.
• HEP2, which completed recruitment, includes 205 participants aged 16 to 65 years old with medication-resistant focal epilepsy in the United States. The study assesses changes in health status, treatments, quality of life, and health care costs. Blood samples are collected at designated points throughout the course of follow-up (target duration of 2 years).
• HEP3 is an ongoing study to identify predictive factors for different treatment responses to seizure medications. Eligible participants are at least 13 years old with newly diagnosed idiopathic generalized epilepsy. Participants will contribute biomarkers over the course of a 2-year follow-up period.

Fragility Multi-Center Retrospective Study

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, 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

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

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

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

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

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

Dataset for event encoded analog EEG signals for detection of Epileptic seizures

This dataset contains events that are encoded from the analog signals recorded during pre-surgical evaluations of patients at the Sleep-Wake-Epilepsy-Center (SWEC) of the University Department of Neurology at the Inselspital Bern. The analog signals are sourced from the SWEC-ETHZ iEEG Database

This database contains event streams for 10 seizures recorded from 5 patients and generated by the DYnamic Neuromorphic Asynchronous Processor (DYNAP-SE2) to demonstrate a proof-of-concept of encoding seizures with network synchronization. The pipeline consists of two parts (I) an Analog Front End (AFE) and (II) an SNN termed as"Non-Local Non-Global" (NLNG) network.

In the first part of the pipeline, the digitally recorded signals from SWEC-ETHZ iEEG Database are converted to analog signals via an 18-bit Digital-to-Analog converter (DAC) and then amplified and encoded into events by an Asynchronous Delta Modulator (ADM). Then in the second part, the encoded event streams are fed into the SNN that extracts the features of the epileptic seizure by extracting the partial synchronous patterns intrinsic to the seizure dynamics.

Details about the neuromorphic processing pipeline and the encoding process are included in a manuscript under review. The preprint is available in bioRxiv

InstallationThe installation requires Python>=3.x and conda (or py-venv) package. Users can then install the requirements inside a conda environment using

conda env create -f requirements.txt -n sez

Once created the conda environment can be activated with conda activate sez

The main files in the database are described in the hierarchy below.

EventSezDataset/

├─ data/

│ ├─ P x S x

│ │ ├─ Pat x Sz x _CH x .csv

├─ LSVM_Params/

│ ├─ opt_svm_params/

│ ├─ pat_x_features_SYNCH/

├─ fig_gen.py

├─ sync_mat_gen.py

├─ SeizDetection_FR.py

├─ SeizDetection_SYNCH.py

├─ support.py

├─ run.sh

├─ requirements.txt

where x represents the Patient ID and the Seizure ID respectively.

requirements.txt: This file lists the requirements for the execution of the Python code.

fig_gen.py: This file plots the analog signals and the associated AFE and NLNG event streams. The execution of the code happens with `python fig_gen.py 1 1 13', where patient 2, seizure 1, and channel 13 of the recording are plotted.

sync_mat_gen.py: This file describes the function for plotting the synchronization matrices emerging from the ADM and the NLNG spikes with either linear or log colorbar. The execution of the code happens with python sync_mat_gen.py 1 1' orpython sync_mat_gen.py 1 1 log'. This execution generated four figures for pre-seizure, First Half of seizure, Second Half of seizure, and post-seizure time periods, where patient 1 and seizure 1. The third option can either be left blank or input as lin or log, for respective color bar scales. The time is the signal-time as mentioned in the table below.

run.sh: A simple Linux script to run the above code for all patients and seizures.

SeizDetection_FR.py: This file runs the LSVM on the ADM and NLNG spikes, using the firing rate (FR) as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/opt_svm_params/ folder). Users can use the code for training the LSVM with different parameters as well.

SeizDetection_SYNCH.py: This file runs the LSVM on the kernelized ADM and NLNG spikes, using the flattened SYNC matrices as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/pat_x_features_SYNCH/ folder). Users can use the code for training the LSVM with different parameters as well.

LSVM_Params: Folder containing LSVM features with different parameter combinations.

support.py: This file contains the necessary functions.

data/P1S1/: This folder, for example, contains the event streams for all channels for seizure 1 of patient 1.

Pat1_Sz_1_CH1.csv: This file contains the spikes of the AFE and the NLNG layers with the following tabular format (which can be extracted by the fig_gen.py)

Comments

SStart: 180 //Start of the Seizure in signal time# SEnd: 276.0 //Start of the Seizure in signal time# Pid: 2 // The patient ID as per the SWEC-ETHZ iEEG Database # Sid: 1 // The Seizure ID as per the SWEC-ETHZ iEEG Database # Channel_No: 1 // The channel number

SYS_time signal_time dac_value ADMspikes NLNGspikes

The time from the interface FPGA The time of the signal as per the SWEC ETHZ Database The value of the analog signals as recorded in the SWEC ETHZ Database The event-steam is the output of the AFE in boolean format. True represents a spike The spike-steam is the output of the SNN in boolean format. True represents a spike

This dataset includes de-identified interictal spike information in 20 focal epilepsy patients who became seizure-free after surgery. This dataset has been used and analyzed to study epilepsy sources and the results are reported in: Sun R, Sohrabpour A, Worrell GA, He B: “Deep Neural Networks Constrained by Neural Mass Models Improve Electrophysiological Source Imaging of Spatiotemporal Brain Dynamics.” Proceedings of the National Academy of Sciences of the United States of America 119.31 (2022): e2201128119.

Please cite the above paper if you use any data included in this dataset. Codes related to this study are also available from (https://github.com/bfinl/DeepSIF). Clinical information of the patients are described in the Supplementary Table S1 of the paper. This dataset was collected under support from the National Institutes of Health via grants NS096761 and EB021027 to Dr. Bin He and Dr. Greg Worrell. The human data were collected and de-identified as part of NIH funded research at Mayo Clinic, Rochester, overseen by Dr. Greg Worrell, and processed and organized at Dr. Bin He’s lab at Carnegie Mellon University. The data are shared for information only. The EEG recorded in this dataset follows a 10-10 system and contains 76 channels of recording. One channel is the reference channel and given that we use a common reference channel for source imaging this channel is removed from the topographical map data and the corresponding rows of the lead-field matrix for each patient, equals 75. The EEG data were filtered between 1-40 Hz and a common average reference was used to pre-process the data.

The data include three variables: * spike_peak_data: Contains the topographical EEG map at the peak of the spike selected in each individual patient. * patient_id: patient index corresponding to the patient ID in Supplementary Table S1 of the paper. * eloc: electrode information in EEGLAB (https://sccn.ucsd.edu/eeglab/index.php) format.

The database consists of EEG recordings of 14 patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena. Subjects include 9 males (ages 25-71) and 5 females (ages 20-58). Subjects were monitored with a Video-EEG with a sampling rate of 512 Hz, with electrodes arranged on the basis of the international 10-20 System. Most of the recordings also contain 1 or 2 EKG signals. The diagnosis of epilepsy and the classification of seizures according to the criteria of the International League Against Epilepsy were performed by an expert clinician after a careful review of the clinical and electrophysiological data of each patient.

This data package contains information on genetic associations including biochemical protein-protein interaction, genetic variation, gene chemical interaction and protein kinase interactome.

Summary: This database, collected at the Neural Engineering Laboratory, Iran University of Science and Technology, comprises iEEG recordings from Wistar rats during healthy and epileptic conditions. Recordings were collected from 5 rats (3 males, 2 females, weighing 260-378 g and aged 4-5 months). iEEG signals were recorded from 3 brain sites: motor cortex (left M1), thalamus (left ANT), and hippocampus (right CA1) of freely moving rats. As a result, for each rat, a matrix with 3 columns (representing the 3 signals) is available in this dataset. To induce temporal lobe epilepsy (TLE), a nonconvulsive pentylenetetrazol (PTZ) model was used. In this epileptic model, rats displayed recurrent spontaneous seizures at Racine stage 1-2, accompanied by spike-wave discharges (SWDs). A seizure can be detected by identifying high-amplitude, repetitive SWDs in the epileptic signals. Recordings were conducted in two conditions: the healthy condition (before PTZ injection) and the epileptic condition (after PTZ injection). Each healthy and epileptic condition contains several trials.

The details of recording and experimental methods can be seen in the paper: Azam Ghanaei and Abbas Erfanian, ''Real-time Seizure Prediction Using Nonlinear Dynamical Analysis of Intracranial EEG and Deep Learning in a Rodent Model of Epilepsy''.

Data files organization: The data files are organized into five main folders, each corresponding to a subject (Rat01, Rat02, Rat03, Rat04, and Rat05). Each main folder contains two subfolders for the healthy and epileptic conditions, named 'Healthy' and 'Epileptic'. Each subfolder contains several .mat files corresponding to the recording trials. Each .mat file contains three iEEG signals from a single recording trial. The name of each .mat file is 'RatID#_E/H_S#,' where 'ID#' refers to the rat’s number, 'E/H' indicates the animal conditions (E for 'Epileptic' and H for 'Healthy'), and 'S#' indicates the experimental trial. For example, 'Rat01_E_01.mat' contains the recorded iEEG signals from the epileptic animal for rat 1, trial 1.

Data format: The data are in MATLAB format (.mat). Each .mat file contains a signal matrix (i.e., 'RatID#_E/H_S#') with n rows and m columns (n×m), representing iEEG signals from a single trial. n is the number of samples, which can be calculated as Sampling Rate × Trial Recording Time. m is the number of channels (m=3) To have signals in the millivolt range, divide the amplitude by 10000.