EEG-based emotion classification using spiking neural networks
Y Luo, Q Fu, J **, learning, visualization, classification, and understanding of fMRI data in the NeuCube evolving spatiotemporal data machine of spiking neural networks
This paper introduces a new methodology for dynamic learning, visualization, and
classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data …
classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data …
Depression identification using eeg signals via a hybrid of lstm and spiking neural networks
Depression severity can be classified into distinct phases based on the Beck depression
inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of …
inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of …
A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not …
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 …
classification, and comparative analysis of brain data. As a case study, the method was …
Map** temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data
This paper proposes a new method for an optimized map** of temporal variables,
describing a temporal stream data, into the recently proposed NeuCube spiking neural …
describing a temporal stream data, into the recently proposed NeuCube spiking neural …
Personalized spiking neural network models of clinical and environmental factors to predict stroke
The high incidence of stroke occurrence necessitates the understanding of its causes and
possible ways for early prediction and prevention. In this respect, statistical methods offer the …
possible ways for early prediction and prevention. In this respect, statistical methods offer the …
Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware
Objective. The objective of this work is to use the capability of spiking neural networks to
capture the spatio-temporal information encoded in time-series signals and decode them …
capture the spatio-temporal information encoded in time-series signals and decode them …
Integrating space, time, and orientation in spiking neural networks: a case study on multimodal brain data modeling
Recent progress in a noninvasive brain data sampling technology has facilitated
simultaneous sampling of multiple modalities of brain data, such as functional magnetic …
simultaneous sampling of multiple modalities of brain data, such as functional magnetic …
Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on …
The paper presents a methodology for the analysis of functional changes in brain activity
across different conditions and different groups of subjects. This analysis is based on the …
across different conditions and different groups of subjects. This analysis is based on the …