88 citations · 132 across the 8 of their papers we have counts for
8 papers
Leveraging Foundation Models for Calibration-Free c-VEP BCIs
Mohammadreza Behboodi, Eli Kinney-Lang, Ali Etemad +2
Foundation Models (FMs) have surged in popularity over the past five years, with applications spanning fields from computer vision to natural language processing. Brain-Computer In…
Narrative-Guided Reinforcement Learning: A Platform for Studying Language Model Influence on Decision Making
Anup Tuladhar, Araz Minhas, Adam Kirton +1
We present a preliminary experimental platform that explores how narrative elements might shape AI decision-making by combining reinforcement learning (RL) with language model reas…
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…
Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control
Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matr…
Higher order tensor decomposition for proportional myoelectric control based on muscle synergies
Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is lim…
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…