3 citations · 6 across the 4 of their papers we have counts for
5 papers
CNNATT: Deep EEG & fNIRS Real-Time Decoding of bimanual forces
Pablo Ortega, Tong Zhao, Aldo Faisal
Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interface…
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing
Denghao Li, Pablo Ortega, Xiaoxi Wei +1
We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-le…
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding
Xiaoxi Wei, Pablo Ortega, A. Aldo Faisal
Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. How…
HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
Pablo Ortega, Aldo Faisal
We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding c…
Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI
Pablo Ortega, Cedric Colas, Aldo Faisal
For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment. Natural tasks often re…