Results 51 to 60 of about 251 (118)

Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks. [PDF]

Schizophrenia (Heidelb), 2023
Doborjeh Z, Doborjeh M, Sumich A, Singh B, Merkin A, Budhraja S, Goh W, Lai EM, Williams M, Tan S, Lee J, Kasabov N.   +11 more
europepmc   +1 more source

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review. [PDF]

Natl Sci Rev, 2023
Jiang N, Chen C, He J, Meng J, Pan L, Su S, Zhu X.   +6 more
europepmc   +1 more source

Spiking Neural Networks and Their Applications: A Review. [PDF]

Brain Sci, 2022
Yamazaki K, Vo-Ho VK, Bulsara D, Le N.
europepmc   +1 more source

Mapping, learning and mining of spatiotemporal brain data with 3D evolving spiking neurogenetic models

, 2014
Spatio- and spectro-temporal data are the most common data in many domain areas, including bioinformatics and neuroinformatics. Still there are no sufficient methods to model such data and to discover complex spatio-temporal patterns from it.
Kasabov, N
core  

Adaptive Methods for Spatiotemporal Stream Data Mining

, 2018
The availability of temporal and spatiotemporal data is increasing, and the use of traditional statistical techniques to deal with such data is insufficient.
Hartono, Reggio Nurtanio
core   +1 more source

Classification of Human Emotional States Based on Valence-Arousal Scale using Electroencephalogram. [PDF]

J Med Signals Sens, 2023
Kumar GS, Sampathila N, Martis RJ.
europepmc   +1 more source

Review of deep learning models with Spiking Neural Networks for modeling and analysis of multimodal neuroimaging data. [PDF]

Front Neurosci
Khan A, Shim V, Fernandez J, Kasabov NK, Wang A.   +4 more
europepmc   +1 more source

Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks. [PDF]

Sensors (Basel), 2023
Wang X, Yang J, Kasabov NK.
europepmc   +1 more source

Mapping, learning and mining of spatiotemporal brain data with 3D evolving spiking neurogenetic models

, 2012
Spatio- and spectro-temporal data are the most common data in many domain areas, including bioinformatics and neuroinformatics. Still there are no sufficient methods to model such data and to discover complex spatio-temporal patterns from it.
Kasabov, N
core  

EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network. [PDF]

Brain Sci, 2022
Dai J, Xi X, Li G, Wang T.
europepmc   +1 more source