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 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.


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.


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.


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.


The provided EEG data were acquired from sixteen healthy young adults (age range 22 - 30 years) with no history of neurological, physical, or psychiatric illness. All the participants were naive BCI users who had not participated in any related experiments before. Informed consents were received from all participants.  The study has been approved by the Institutional Research Ethics Committee of Nazarbayev University.  



Mobile Brain-Body Imaging (MoBI) technology was deployed at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México, in an effort to collect Electroencefalographic (EEG) data from large numbers (N = ~1200) of participants and allow the study of the brain’s response to artistic stimuli, as part of the studies developed by University of Houston (TX, USA) and Tecnológico de Monterrey (MTY, México).


This dataset consists of raw EEG data from 48 subjects who participated in a multitasking workload experiment utilizing the SIMKAP multitasking test. The subjects’ brain activity at rest was also recorded before the test and is included as well. The Emotiv EPOC device, with sampling frequency of 128Hz and 14 channels was used to obtain the data, with 2.5 minutes of EEG recording for each case. Subjects were also asked to rate their perceived mental workload after each stage on a rating scale of 1 to 9 and the ratings are provided in a separate file.


This hackathon is co-located with the 42nd IEEE International Conference on Computers, Software & Application. The hackathon event will take place July 23-24 in Tokyo, Japan.
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Last Updated On: 
Tue, 04/02/2019 - 11:51
Citation Author(s): 
J.A. Anguera, J. Boccanfuso, J.L. Rintoul, O. Al-Hashimi, F. Faraji, J. Janowich, E. Kong, Y.Larraburo, C. Rolle, E. Johnston and A. Gazzaley