Brain

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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  • Biomedical and Health Sciences
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38

    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.

    545 views
  • Biomedical and Health Sciences
  • Last Updated On: 
    Sat, 06/16/2018 - 23:05

    This dataset contains light-field microscopy images and converted sub-aperture images. 

     

    The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.

     

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  • Image Processing
  • Last Updated On: 
    Thu, 01/09/2020 - 12:42

    This databases includes brain tumour images of both malignant and benign type. 

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  • Brain
  • Last Updated On: 
    Thu, 12/26/2019 - 22:01

    BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019). 

    198 views
  • Machine Learning
  • Last Updated On: 
    Thu, 12/12/2019 - 13:17

    Complex networks have been successfully applied to sleep stage analysis and classification. However, whether the electroencephalogram (EEG) montage reference will affect the network properties is still unclear.

    97 views
  • Brain
  • Last Updated On: 
    Wed, 10/02/2019 - 05:37

    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.

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  • Brain
  • Last Updated On: 
    Mon, 08/12/2019 - 11:24

    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.  

     

    704 views
  • Medical Imaging
  • Last Updated On: 
    Sun, 05/19/2019 - 08:11

     

    This material is associated with the PhD Thesis of Javier Olias (which is supervised by Sergio Cruces) and the article:
    EEG Signal Processing in MI-BCI Applications with Improved Covariance Matrix Estimators
    by J.Olias, R. Martin-Clemente, M.A. Sarmiento-Vega and S. Cruces,
    which was accepted in 2019 by IEEE Transactions on Neural Systems and Rehabilitation Engineering.

    448 views
  • Brain
  • Last Updated On: 
    Thu, 03/14/2019 - 06:58

    Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use on-line neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance.

    403 views
  • Neuroscience
  • Last Updated On: 
    Mon, 03/04/2019 - 05:35

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