Results 81 to 90 of about 142 (100)

Spatio-temporal EEG Data Classification in the NeuCube 3D SNN Environment: Methodology and Examples

2013
A vast amount of complex spatio-temporal brain data, such as EEG-, have been accumulated. Technological advances in many disciplines rely on the proper analysis, understanding and utilisation of these data. In order to address this great challenge, the paper utilizes the recently introduced by one of the authors 3D spiking neural network environment ...
Nikola K. Kasabov, Jin Hu, Yixiong Chen, Nathan Matthew Scott, Yulia Turkova   +4 more
openaire   +1 more source

EEG Comparison Between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube

2017
This paper is a feasibility study of using the NeuCube spiking neural network (SNN) architecture for modeling EEG brain data related to perceiving versus mimicking facial expressions. We collected EEG patterns during perception and imitation of facial expressions for each emotion.
Yuma Omori, Hideaki Kawano, Akinori Seo, Zohreh Gholami Doborjeh, Nikola K. Kasabov, Maryam Gholami Doborjeh   +5 more
openaire   +1 more source

Longitudinal study of alzheimer's disease degeneration through EEG data analysis with a NeuCube spiking neural network model

2016 International Joint Conference on Neural Networks (IJCNN), 2016
Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroen-cephalography (EEG) data collected from people affected by Alzheimer's Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse ...
Elisa Capecci, Zohreh Gholami Doborjeh, Nadia Mammone, Fabio La Foresta, Francesco Carlo Morabito, Nikola K. Kasabov   +5 more
openaire   +1 more source

Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2017
This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1].
Zohreh Doborjeh, Nikola Kasabov, Maryam Doborjeh   +2 more
exaly   +3 more sources

Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression

2019
In the recent years, machine learning and deep learning techniques are being applied on brain data to study mental health. The activation of neurons in these models is static and continuous-valued. However, a biological neuron processes the information in the form of discrete spikes based on the spike time and the firing rate.
Dhvani Shah, Grace Y. Wang, Maryam Doborjeh, Zohreh Gholami Doborjeh, Nikola K. Kasabov   +4 more
openaire   +1 more source

Efficient Recognition of Attentional Bias Using EEG Data and the NeuCube Evolving Spatio-Temporal Data Machine

2016
Modelling of dynamic brain activity for better understanding of human decision making processes becomes an important task in many areas of study. Inspired by importance of the attentional bias principle in human choice behaviour, we proposed a Spiking Neural Network (SNN) model for efficient recognition of attentional bias.
Zohreh Gholami Doborjeh, Maryam Gholami Doborjeh, Nikola K. Kasabov   +2 more
openaire   +1 more source

Which method to use for optimal structure and function representation of large spiking neural networks: A case study on the NeuCube architecture

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This study analyses different representations of large spiking neural network (SNN) structures for conventional computers and uses the NeuCube SNN architecture as a case study. The representation includes neuronal connectivity and network's and neurons' states during the learning process.
Anne Abbott, Neelava Sengupta, Nikola K. Kasabov   +2 more
openaire   +1 more source

NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data

Neural Networks, 2014
The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular ...
openaire   +2 more sources

Analysis of Gene Expression Time Series Data of Ebola Vaccine response using the NeuCube and Temporal Feature Selection

2018 International Joint Conference on Neural Networks (IJCNN), 2018
The purpose of this paper was to investigate a pipeline for processing temporal gene expression data using spiking neural networks and temporal feature selection techniques that would allow for genomic marker discovery. A promising temporal feature selection method was tested using the NeuCube for classification against a set of previously identified ...
Lucien Koefoed, Elisa Capecci, Nikola K. Kasabov   +2 more
openaire   +1 more source

FaNeuRobot: A Framework for Robot and Prosthetics Control Using the NeuCube Spiking Neural Network Architecture and Finite Automata Theory

2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
Limb amputation is a global problem. Prosthetic limbs can enhance the quality of life of amputees. To this end, anthropomorphic design and intuitive manipulation are two essential requirements. This paper presents a motor control framework for prosthetic control through Brain-Machine Interface (BMI) using Finite Automata Theory, and NeuCube Evolving ...
Kaushalya Kumarasinghe, Mahonri Owen, Denise Taylor, Nikola K. Kasabov, Chi Kit   +4 more
openaire   +1 more source