One of the grand challenges in neuroscience is to understand the developing brain ‘in action and in context’ in complex natural settings. To address this challenge, it is imperative to acquire brain data from freely-behaving children to assay the variability and individuality of neural patterns across gender and age.
Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies. Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.
This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **
All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called README.md in that repository.
You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here: http://ieeexplore.ieee.org/document/7742994/
Recent advances in computational power availibility and cloud computing has prompted extensive research in epileptic seizure detection and prediction. EEG (electroencephalogram) datasets from ‘Dept. of Epileptology, 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.
- The tool used for preprocessing is Anaconda-Jupyter Notebook on Intel 8th gen i5 processor with 8GB RAM
- The dataset is prepared by extracting datapoints from '.edf' by using mne package in python. Equal amount of preictal and ictal data are extracted.
- A period of 4096 seconds (68 minutes) each of preictal and ictal data is extracted from the '.edf' files. All ictal periods for 24 patients annotated have been included in the dataset.
- Datapoints are loaded and preprocessed as dataframes by using pandas package in python.
- System RAM size should be available to the maximum possible extent as dataframes are large.
- The file chbmit_preprocessed_data.csv can be used as is for machine learning and deep learning models.
Data Availability :
The datset contains following files.
- chbmit_ictal_raw_data.csv : This file contains only ictal data from all 24 patients. The channels vary largely and amount to 96 columns in this file.
- chbmit_preictal_raw_data.csv : This file contains only preictal data from all 24 patients. The channels vary largely and amount to 96 columns in this file.
- chbmit_preictal_23channels_data.csv :This file contains only preictal data from all 24 patients. Only 23 channels are retained and amount to 23 columns in this file.
- chbmit_ictal_23channels_data.csv :This file contains only ictal data from all 24 patients. Only 23 channels are retained and amount to 23 columns in this file.
- chbmit_preprocessed_data.csv :This file contains balanced preictal and ictal data from all 24 patients. Only 23 channels are retained, outcome column is added and amount to 24 columns in this file. In outcome column '0' indicates preictal and '1' indicates ictal.
This dataset is prepared with data reduction techniques. Data cleaning and data transformation need to be done as suitable for the application or model under development.
The original raw dataset in '.edf' is available at https://physionet.org/content/chbmit/1.0.0/ and to be cited as
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220
The MAUS dataset focused on collecting easy-acquired physiological signals under different mental demand conditions. We used the N-back task to stimuli different mental workload statuses. This dataset can help in developing a mental workload assessment system based on wearable device, especially for that PPG-based system. MAUS dataset provides ECG, Fingertip-PPG, Wrist-PPG, and GSR signal. User can make their own comparison between Fingertip-PPG and Wrist-PPG. Some study can be carried out in this dataset
The database is organized in 2 folders and documentation:
• Data – raw signal recordings for the individual participants, including extracted Inter-Beat-Interval sequence and participants’ respond in N-back task
• Subjective_rating – subjective rating of sleep quality and NASA-TLX
• MAUS_Documentation.pdf – documentation of dataset description and details.
The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment.
The data files are stored in a comma-separated value (.csv) format.
60 sample files of activities performed by healthy individuals and 30 sample files of activities performed by epileptic patients are present in two separate folders in the .zip file.
The sampling frequency of the headset is 512Hz and each activity is performed for a duration of 20 seconds. Every data file contains raw EEG data in a single column.
Disclaimer: This data was collected ethically with the consent of relevant local research committees. The anonymity of subjects and confidentiality of their mental health conditions was ensured.
The data acquisition process begins with capturing EEG signals from 20 healthy skilled volunteers who gave their written consent before performing the experiments. Each volunteer was asked to repeat an experiment for 10 times at different frequencies; each experiment was trigger by a visual stimulus.
University level education is in constant evolution, making use of technological advancements in order to provide high quality pedagogical instruction to students. An important aspect of modern education is the contribution of Information Technologies,as different technological resources can be implemented to provide education under a variety of teaching modalities.
Solving continuous non-invasive blood pressure monitoring with wearable sensors has the potential to reform hypertension diagnosis and management. This approach has proven to be non-trivial and many cardiovascular sensing modalities have fallen short of accomplishing the task. This dataset consists of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and millimeter-wave radar. Data collection was conducted under set protocol with subjects at rest.
This dataset contains data collected during a study led by Blumio, Inc. and supported by the Center for Disease Control and Infineon Technologies AG. Each CSV file contains the synchronised sensor data from a single study participant. The CSV file is named for that participant's study identifier. Each column of the CSV contains output from a wearable sensor in this order: timestamp in seconds, continuous non-invasive blood pressure reference, photoplethysmogram, applanation tonometry, radar processed with Blumio's proprietary algorithm, and radar processed with a standard phase transformation. Also included is a XLSX summary sheet of the participants' health data and signal quality labels.