AIMC Topic: Photic Stimulation
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Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.
Scientific reports
Feb 28, 2018
The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to pr...
Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework.
Artificial intelligence in medicine
Feb 1, 2018
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data....
A border-ownership model based on computational electromagnetism.
Neural networks : the official journal of the International Neural Network Society
Jan 10, 2018
The mathematical relation between a vector electric field and its corresponding scalar potential field is useful to formulate computational problems of lower/middle-order visual processing, specifically related to the assignment of borders to the sid...
STDP-based spiking deep convolutional neural networks for object recognition.
Neural networks : the official journal of the International Neural Network Society
Dec 23, 2017
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively sha...
Decoding of finger trajectory from ECoG using deep learning.
Journal of neural engineering
Nov 28, 2017
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...
Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.
Journal of computational neuroscience
Nov 15, 2017
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean...
Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.
Scientific reports
Nov 8, 2017
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase ...
A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot.
Journal of healthcare engineering
Sep 17, 2017
OBJECTIVE: This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility.
Ankle
Virtual Reality
Photic Stimulation
Brain-Computer Interfaces
Robotics
Fourier Analysis
Reproducibility of Results
Humans
Motion
Evoked Potentials, Visual
Rehabilitation
Electroencephalography
Young Adult
Algorithms
Healthy Volunteers
Ankle Joint
Male
Signal Processing, Computer-Assisted
Female
Feasibility Studies
Deep Manifold Learning Combined With Convolutional Neural Networks for Action Recognition.
IEEE transactions on neural networks and learning systems
Sep 15, 2017
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and ef...
A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.
Methods (San Diego, Calif.)
Jun 22, 2017
EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation...
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