Neuroscience

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.

  • Neuroscience
  • Biophysiological Signals
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Akshay Sujatha Ravindran, Jesus G. Cruz-Garza, Anastasiya Kopteva, Andrew Paek, Aryan Mobiny, Zachary Hernandez, Jose Luis Contreras-Vidal

    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.

  • Biomedical and Health Sciences
  • Neuroscience
  • Medical Imaging
  • Biophysiological Signals
  • Brain
  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Jesus G. Cruz-Garza, Justin A Brantley, Sho Nakagome, Kim Kontson, Dario Robleto, Jose L. Contreras-Vidal

    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.

  • Biomedical and Health Sciences
  • Neuroscience
  • Biophysiological Signals
  • Brain
  • Signal Processing
  • Last Updated On: 
    Sat, 06/16/2018 - 23:05
    Citation Author(s): 
    Andrew Jackson, Thomas M. Hall

    The dataset, includes raw data, observations and biometric data from our case study with an individual with DMD, controlling for the first time an active hand orthosis.

  • Biomedical and Health Sciences
  • Neuroscience
  • Biophysiological Signals
  • Signal Processing
  • Last Updated On: 
    Wed, 03/13/2019 - 11:40

    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.

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

    This dataset collection contains eleven datasets used in Locally Linear Embedding and fMRI feature selection in psychiatric classification.

    The datasets given in the Links section are reduced subsets of those contained in their respective tar files (a consequence of Mendeley Data's 10GB limitation).

    The Linked datasets (not the tar files) contain just the MATLAB file and the resting state image (or block-design fMRI for the MRN dataset), where appropriate.

  • Neuroscience
  • Medical Imaging
  • Signal Processing
  • Last Updated On: 
    Sun, 02/17/2019 - 13:07

    This code was used in some previous our articles, such as:

  • Image Fusion
  • Biomedical and Health Sciences
  • Neuroscience
  • Medical Imaging
  • Biophysiological Signals
  • Brain
  • Signal Processing
  • Last Updated On: 
    Wed, 01/16/2019 - 03:45

    NGM software for applied neurogoniometry. See our previous articles.

  • Image Fusion
  • Biomedical and Health Sciences
  • Neuroscience
  • Medical Imaging
  • Biophysiological Signals
  • Brain
  • Last Updated On: 
    Wed, 01/16/2019 - 03:35

    See .doc-fie in the attachement.

  • Image Fusion
  • Neuroscience
  • Medical Imaging
  • Biophysiological Signals
  • Brain
  • Signal Processing
  • Last Updated On: 
    Wed, 01/16/2019 - 03:15

    The Ionic Polymer Metal Composite (IPMC) actuator is a group of Electro-Active Polymer (EAP) which bends in response to a relatively low electrical voltage because of the motion of cations in the polymer network. IPMC has a wide range of applications in robotics, biomedical devices and artificial muscles. This paper presents a fuzzy logic approach to the electromyography (EMG) pattern recognition for an IPMC actuating system with the EMG signal.

  • Neuroscience
  • Last Updated On: 
    Tue, 01/15/2019 - 16:10

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