NGM software for applied neurogoniometry. See our previous articles.

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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).

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The dataset can be used for Brain MRI study for academic purpose only. Undersampling Masks can be used for random undersampling at different sampling rates. 

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Violin melody clips expressing happiness, sadness, threat, excitement, and neutrality. Both electric and acoustic violin.

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The IEEE Brain Initiative is partnering with the 3rd IEEE Big Data Governance and Metadata Management Workshop (BDGMM) to host Hackathon Track #2, Brain Data Bank on Video Gaming Enhances Cognitive Skills. The BDGMM Workshop will be held in conjunction with IEEE Big Data 2018 in Seattle, Washington, and the hackathon will take place 10-11 December 2018. Cash awards will be given to top places. Registration link is provided here: https://bigdatawg.nist.gov/bdmm2018.html

Last Updated On: 
Mon, 12/10/2018 - 20:07
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

Videomicroscopic Semi-Shadow Visualization of LoC-Si (Lab-on-a-Chip_[based_on]_Silicone) test structures from Institute of Molecular Electronics (D. Shevchenko; founder of Scientific and Production Association "Microbiotechniques" Ltd.) and Russian Academy of Sciences (INEPCP RAS; ICP RAS)

 

Vis. Tech.: MBS-10 Binocular Stereoscopic Microscope; Indirect Angular Illumination.

 

Found.: Initiative project (D. Shevchenko, O. Gradov; 2015-2016)

 

Fab.: JSC “Voskhod” KRLZ*

 

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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.

Instructions: 

The data for each subject follows the naming convention: subno_task.txt. For example, sub01_lo.txt would be raw EEG data for subject 1 at rest, while sub23_hi.txt would be raw EEG data for subject 23 during the multitasking test. The rows of each datafile corresponds to the samples in the recording and the columns corresponds to the 14 channels of the EEG device: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, respectively.

The ratings for each subject is given in a separate file ratings.txt. They are given in a comma separated value format: subject number, rating at rest, rating for test. For example: 1, 2, 8 would be subject 1, rating of 2 for “at rest”, rating of 8 for “test”. Note that ratings for subjects 5, 24 and 42 are unavailable.

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MI dataset for deep learning approach

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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.
Register here: https://ieeecompsac.computer.org/2018/big-data-hackathon-registration/
More details: https://bigdatawg.nist.gov/bdgmm_compsac2018.html

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

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