24 citations · 33 across the 7 of their papers we have counts for
7 papers
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding
Xiaoxi Wei, Jyotindra Narayan, A. Aldo Faisal
Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasiv…
Survey of Consciousness Theory from Computational Perspective
Zihan Ding, Xiaoxi Wei, Yidan Xu
Human consciousness has been a long-lasting mystery for centuries, while machine intelligence and consciousness is an arduous pursuit. Researchers have developed diverse theories f…
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
Jinpei Han, Xiaoxi Wei, A. Aldo Faisal
Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavaila…
Federated deep transfer learning for EEG decoding using multiple BCI tasks
Xiaoxi Wei, A. Aldo Faisal
Deep learning has been successful in BCI decoding. However, it is very data-hungry and requires pooling data from multiple sources. EEG data from various sources decrease the decod…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
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…