Results 31 to 40 of about 251 (118)

Feasibility Analysis of Using NeuCube 3D SNN Environment for Spatio-Temporal EEG Data Classification Related to Perception of Art

, 2014
This thesis is a feasibility study of using a Spiking Neural Network (SNN) architecture named NeuCube for the classification of electroencephalography (EEG) data related to the perception of art. We have performed classification of human brain perception
Turkova, Yulia
core   +1 more source

Brain-Computer Interfaces for Virtual Quadcopters Based on a Spiking-Neural Network Architecture - NeuCube

, 2015
A novel framework is proposed in this study that uses a spiking neural network for learning spatio-temporal and spectro-temporal data called NeuCube. It is capable of learning and classifying such data in real time (online).
Gollahalli, Akshay Raj
core  

New Algorithms for Encoding, Learning and Classification of fMRI Data in a Dpiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive [PDF]

, 2016
The paper argues that, the third generation of neural networks – the spiking neural networks (SNN), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging (fMRI) data. The paper proposes
Jie Yang, Doborjeh, Zohreh, Gholami, Z, Doborjeh, Maryam, Doborjeh, M, Kasabov, N, Kasabov, Nikola, Zhou, Lei, Yang, Jie, Zhou, L   +9 more
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EEG-Based Depression Classification and Brain Region Analysis Using a Hybrid of NeuCube and Dictionary Learning Framework

Abstract The Study of depression and its effects on the brain is essential since this common mental health disorder affects millions. In addition to disturbing emotional and cognitive processes, depression also disrupts activity in discrete brain regions. Identifying these distortions is important for expanding the diagnosis and treatment plans.
Ali Sam, F. Alenizi, B. Bouallegue, Reza Boostani   +3 more
openaire   +2 more sources

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects

, 2016
This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive
Russell, Bruce, Kasabov, Nikola K., Kydd, Robert, Doborjeh, Maryam Gholami, Wang, Grace Y.   +4 more
core   +1 more source

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? [PDF]

Brain Sci, 2023
Pham MD, D'Angiulli A, Dehnavi MM, Chhabra R.   +3 more
europepmc   +1 more source

Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks

, 2018
Publisher Copyright: © 2018, Springer Nature Switzerland AG.Analysis of temporal gene expression data poses a significant challenge due to the combination of high dimensionality and low sample size.
Koefoed, Lucien, Durgesh Nandini, Ibai Laña, Elisa Capecci, Laña, Ibai, Shahi, Gautam Kishore, Gautam Kishore Shahi, Kasabov, Nikola, Nandini, Durgesh, Lucien Koefoed, Nikola Kasabov, Capecci, Elisa   +11 more
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Prediction and detection of virtual reality induced cybersickness: a spiking neural network approach using spatiotemporal EEG brain data and heart rate variability. [PDF]

Brain Inform, 2023
Yang AHX, Kasabov NK, Cakmak YO.
europepmc   +1 more source

Spiking neural networks for predictive and explainable modelling of multimodal streaming data with a case study on financial time series and online news. [PDF]

Sci Rep, 2023
AbouHassan I, Kasabov NK, Jagtap V, Kulkarni P.   +3 more
europepmc   +1 more source

Diagnostic biomarker discovery from brain EEG data with LSTM, reservoir-SNN and NeuCube: Methods and a pilot study on epilepsy vs migraine

, 2023
<p>The paper explores how deep LSTM and deep spiking neural networks (SNN) can be used to extract meaningful features from spatio-temporal EEG brain data for early, on-line diagnosis. It introduces a new online spike encoding algorithm for Izhikevich neural networks and new methods for learning and diagnostic biomarker discovery for each of the ...
openaire   +1 more source