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

, , , , and . Sensors, 21 (14): 4900 (2021)

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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., , , , and . IEEE Trans. Biomed. Eng., 63 (9): 1830-1841 (2016)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., , and . Neural Networks, (2015)Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network., , , , and . Sensors, 21 (14): 4900 (2021)Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning., , , , , , , , and . Sensors, 23 (2): 902 (January 2023)Deep Learning of EEG Data in the NeuCube Brain-Inspired Spiking Neural Network Architecture for a Better Understanding of Depression., , , , and . ICONIP (3), volume 11955 of Lecture Notes in Computer Science, page 195-206. Springer, (2019)Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture., , , , , , , , , and 2 other author(s). Sensors, 20 (24): 7354 (2020)Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling., , , and . IEEE Access, (2021)Ensemble plasticity and network adaptability in SNNs., , , and . CoRR, (2022)