Grace Y. Wang

Orcid: 0000-0003-2063-031X

Affiliations:
  • Auckland University of Technology, Department of Psychology and Neuroscience, New Zealand (PhD)


According to our database1, Grace Y. Wang authored at least 11 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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2024
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Legend:

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Links

Online presence:

On csauthors.net:

Bibliography

2024
Sentiment Analysis for Detection of Depressive Users on Social Networks.
Proceedings of the 11th International Conference on Behavioural and Social Computing, 2024

2023
Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.
Sensors, January, 2023

2022
Ensemble plasticity and network adaptability in SNNs.
CoRR, 2022

2021
Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.
Sensors, 2021

Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling.
IEEE Access, 2021

2020
Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.
Sensors, 2020

Brain Signal Classification Based on Deep CNN.
Int. J. Secur. Priv. Pervasive Comput., 2020

2019
Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression.
Proceedings of the Neural Information Processing - 26th International Conference, 2019

2016
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.
IEEE Trans. Biomed. Eng., 2016

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.
Neural Networks, 2016

2015
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 opiate dependence treatment.
Neural Networks, 2015


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