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Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCIapplications

https://doi.org/10.1109/ijcnn.2014.6889936
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/1097-0193(200007)10:3<120::aid-hbm30>3.0.co;2-8openalex-snapshot
Automated Talairach Atlas labels for functional brain mapping
inferredcites →https://doi.org/10.1002/mus.23699openalex-snapshot
Spasticity, weakness, force variability, and sustained spontaneous motor unit discharges of resting spastic–paretic biceps brachii muscles in chronic stroke
inferredcites →https://doi.org/10.1006/nimg.2001.0832openalex-snapshot
Neural Simulation of Action: A Unifying Mechanism for Motor Cognition
inferredcites →https://doi.org/10.1007/978-3-642-33212-8_21openalex-snapshot
NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals
inferredcites →https://doi.org/10.1007/978-3-642-42051-1_10openalex-snapshot
NeuCubeRehab: A Pilot Study for EEG Classification in Rehabilitation Practice Based on Spiking Neural Networks
inferredcites →https://doi.org/10.1007/978-3-642-42051-1_11openalex-snapshot
NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation
inferredcites →https://doi.org/10.1016/j.jcomdis.2011.04.011openalex-snapshot
Experience-dependent neural plasticity in the adult damaged brain
inferredcites →https://doi.org/10.1016/j.neucom.2013.09.049openalex-snapshot
Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke
inferredcites →https://doi.org/10.1016/j.neunet.2014.01.006openalex-snapshot
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
inferredcites →https://doi.org/10.1017/s0140525x00034026openalex-snapshot
The representing brain: Neural correlates of motor intention and imagery
inferredcites →https://doi.org/10.1017/s0140525x04000093openalex-snapshot
The emulation theory of representation: Motor control, imagery, and perception
inferredcites →https://doi.org/10.1023/b:naco.0000027755.02868.60openalex-snapshot
The evidence for neural information processing with precise spike-times: A survey
inferredcites →https://doi.org/10.1037/13179-003openalex-snapshot
The organization of behavior.
inferredcites →https://doi.org/10.1038/35090060openalex-snapshot
Neurophysiological mechanisms underlying the understanding and imitation of action
inferredcites →https://doi.org/10.1044/1092-4388(2008/018)openalex-snapshot
Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation After Brain Damage
inferredcites →https://doi.org/10.1098/rstb.2008.0252openalex-snapshot
Experience-dependent plasticity mechanisms for neural rehabilitation in somatosensory cortex
inferredcites →https://doi.org/10.1109/ijcnn.2003.1224019openalex-snapshot
BSA, a fast and accurate spike train encoding scheme
inferredcites →https://doi.org/10.1109/jssc.2007.914337openalex-snapshot
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
inferredcites →https://doi.org/10.1109/tbme.2011.2131142openalex-snapshot
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI
inferredcites →https://doi.org/10.1142/s0129065712500128openalex-snapshot
SPAN: SPIKE PATTERN ASSOCIATION NEURON FOR LEARNING SPATIO-TEMPORAL SPIKE PATTERNS
inferredcites →https://doi.org/10.1152/jn.1995.73.6.2608openalex-snapshot
Motor facilitation during action observation: a magnetic stimulation study
inferredcites →https://doi.org/10.1186/1743-0003-10-4openalex-snapshot
Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study
inferredcites →https://doi.org/10.1186/1743-0003-8-34openalex-snapshot
Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study
inferredcites →https://doi.org/10.1191/026921501672063235openalex-snapshot
A randomized efficacy and feasibility study of imagery in acute stroke
inferredcites →https://doi.org/10.3138/ptc.2011-03openalex-snapshot
Factors That Influence Muscle Weakness Following Stroke and Their Clinical Implications: A Critical Review
inferredcites →https://doi.org/10.3233/nre-2011-0639openalex-snapshot
Recovery of upper limb dexterity in patients more than 1 year after stroke: Frequency, clinical correlates and predictors
inferredcites →https://doi.org/10.3389/fnsyn.2011.00004openalex-snapshot
A history of spike-timing-dependent plasticity
inferredcites →https://doi.org/10.4324/9781410612403openalex-snapshot
The Organization of Behavior

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4617064openalex-extracted
Integrating Local STDP and Global Backpropagation Learning Using Spike-Time and Spike-Rate Representations for Improved Classification of Spatiotemporal Data in the Neucube Brain Inspired Evolving Spiking Neural Network Architecture: A Case Study on EEG Brain Data
inferred← citeshttps://doi.org/10.1007/978-3-319-18164-6_16openalex-snapshot
A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data
inferred← citeshttps://doi.org/10.1007/s11063-020-10322-8openalex-snapshot
Spiking Neural Networks: Background, Recent Development and the NeuCube Architecture
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.12.098openalex-snapshot
NeuroSense: Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns
inferred← citeshttps://doi.org/10.1016/j.neunet.2015.03.009openalex-snapshot
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
inferred← citeshttps://doi.org/10.1016/j.neunet.2015.09.011openalex-snapshot
Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
inferred← citeshttps://doi.org/10.1016/j.neunet.2019.08.029openalex-snapshot
Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces
inferred← citeshttps://doi.org/10.1088/1741-2552/aafabcopenalex-snapshot
Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware
inferred← citeshttps://doi.org/10.1109/access.2020.2978163openalex-snapshot
EEG-Based Emotion Classification Using Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/access.2021.3099492openalex-snapshot
Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling
inferred← citeshttps://doi.org/10.1109/ijcnn.2016.7727357openalex-snapshot
Which method to use for optimal structure and function representation of large spiking neural networks: A case study on the NeuCube architecture
inferred← citeshttps://doi.org/10.1109/tnnls.2016.2536742openalex-snapshot
Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data
inferred← citeshttps://doi.org/10.3390/bioengineering12060628openalex-snapshot
Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture
inferred← citeshttps://doi.org/10.48550/arxiv.1603.05594openalex-snapshot
Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data
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