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.

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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.

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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. 

Instructions: 

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.

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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.

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Reliable fatigue assessment is desired in many different fields and environments. An efficient fatigue evaluation tool is promising in reducing fatal errors and economic loss in industrial settings. This dataset contains electroencephalographic (EEG) signals obtainedfrom an 8-channel OpenBCI headset, as well as biometric measurements obtained from the Empatica E4 wristband. Signals obtained from the E4 include: Photopletismography (PPG), heart rate, inter-beat interval (IBI), skin temperature and Electrodermic Activity(EDA).

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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)

 

"Object classification from randomized EEG trials"

 

If you use this code or data, please cite the above paper.

Instructions: 

See the paper "Object classification from randomized EEG trials" on IEEE Xplore.

 

Code for analyzing the dataset is included in the online supplementary materials for the paper.

 

The code from the online supplementary materials is also included here.

 

If you use this code or data, please cite the above paper.

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Dataset description

This dataset contains EEG signals from 73 subjects (42 healthy; 31 disabled) using an ERP-based speller to control different brain-computer interface (BCI) applications. The demographics of the dataset can be found in info.txt. Additionally, you will find the results of the original study broken down by subject, the code to build the deep-learning models used in [1] (i.e., EEG-Inception, EEGNet, DeepConvNet, CNN-BLSTM) and a script to load the dataset.

Original article:

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Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence

"The perils and pitfalls of block design for EEG classification experiments"

DOI: 10.1109/TPAMI.2020.2973153

If you use this code or data, please cite the above paper.

Instructions: 

See the paper "The perils and pitfalls of block design for EEG classification experiments" on IEEE Xplore.

DOI: 10.1109/TPAMI.2020.2973153

Code for analyzing the dataset is included in the online supplementary materials for the paper.

The code and the appendix from the online supplementary materials are also included here.

If you use this code or data, please cite the above paper.

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EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.

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The dataset consists of EEG recordings obtained when subjects are listening to different utterances : a, i, u, bed, please, sad. A limited number of EEG recordings where also obtained when the three vowels were corrupted by white and babble noise at an SNR of 0dB. Recordings were performed on 8 healthy subjects.

Instructions: 

Recordings were performed at the Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), Sherbrooke (Quebec), Canada. The EEG recordings were performed using an actiCAP active electrode system Version I and II (Brain Products GmbH, Germany) that includes 64 Ag/AgCl electrodes. The signal was amplified with BrainAmp MR amplifiers and recorded using the Vision Recorder software. The electrodes were positioned using a standard 10-20 layout. Experiments were performed on 8 healthy subjects without any declared hearing impairment. Each session lasted approximately 90 minutes and was separated in 2 parts. The first part, lasting 30 minutes, consisted in installing the cap on the subject where an electroconductive gel was placed under each electrode to ensure a proper contact between the electrode and the scalp. The second part, which was the listening and EEG acquisition, lasted approximately 60 minutes. The subjects then had to stay still with eyes closed while avoiding any facial movement or swallowing. They had to remain concentrated on the audio signals during the full length of the experiment. Audio signals were presented to the subjects through earphones while EEGs were recorded. During the experiment, each trial was repeated randomly at least 80 times. A stimulus was presented randomly within each trial which lasted approximately 9 seconds. A 2-minute pause was given after 5 minutes of trials where the subjects could relax and stretch. Once the EEG signals were acquired, they were resampled at 500 Hz and band-pass filtered between 0.1 Hz and 45 Hz in order to extract the frequency bands of interest for this study. EEG signals were then separated into 2-second intervals where the stimulus was presented at 0.5 second within each interval. If the signal amplitude exceeded a pre-defined 75 V limit, the trial was marked for rejection. A sample code is provided to read the dataset and generate ERPs. One needs first to run the epoch_data.m for the specific subject and then run the mean_data.m file in the ERP folder. EEGLab for Matlab is required.

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