Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis

no code implementations • 3 May 2022 • Yun Li, Zhe Liu, Lina Yao, Jessica J. M. Monaghan, David Mcalpine

The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification.

EEG Unsupervised Domain Adaptation

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

no code implementations • 3 May 2022 • Yun Li, Zhe Liu, Lina Yao, Molly Lucas, Jessica J. M. Monaghan, Yu Zhang

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.

BIG-bench Machine Learning EEG +1

Deep reinforcement learning guided graph neural networks for brain network analysis

no code implementations • 18 Mar 2022 • Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning Reinforcement Learning (RL) +1