activity
20172020
most citedOn the use of higher-order tensors to model muscle synergies

7 citations · 7 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SP20207 cited

On the use of higher-order tensors to model muscle synergies

Ahmed Ebied, Loukianos Spyrou, Eli Kinney-Lang +1

The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current mu…

eess.SP2018

Evaluation of matrix factorisation approaches for muscle synergy extraction

Ahmed Ebied, Eli Kinney-Lang, Loukianos Spyrou +1

The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation tec…

eess.SP2018

Muscle Activity Analysis using Higher-Order Tensor Models: Application to Muscle Synergy Identification

Ahmed Ebied, Eli Kinney-lang, Loukianos Spyrou +1

Higher-order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets naturally occurring as multi-way structure…

q-bio.NC2017

Tensor-driven extraction of developmental features from varying paediatric EEG datasets

Eli Kinney-Lang, Loukianos Spyrou, Ahmed Ebied +2

Objective. Consistently changing physiological properties in developing children's brains challenges new data heavy technologies, like brain-computer interfaces (BCI). Advancing si…

cs.CE2017

Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG

Loukianos Spyrou, Mario Parra, Javier Escudero

Objective: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate brain connectivit…