A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


Citation

BibTex format

@inproceedings{Han:2021,
author = {Han, J and Gu, X and Lo, B},
publisher = {IEEE},
title = {Semi-supervised contrastive learning for generalizable motor imagery eeg classification},
url = {http://hdl.handle.net/10044/1/90332},
year = {2021}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Electroencephalography (EEG) is one of the most widely used brain-activity recording methods in non-invasive brain-machine interfaces (BCIs). However, EEG data is highly nonlinear, and its datasets often suffer from issues such as data heterogeneity, label uncertainty and data/label scarcity. To address these, we propose a domain independent, end-to-end semi-supervised learning framework with contrastive learning and adversarial training strategies. Our method was evaluated in experiments with different amounts of labels and an ablation study in a motor imagery EEG dataset. The experiments demonstrate that the proposed framework with two different backbone deep neural networks show improved performance over their supervised counterparts under the same condition.
AU - Han,J
AU - Gu,X
AU - Lo,B
PB - IEEE
PY - 2021///
TI - Semi-supervised contrastive learning for generalizable motor imagery eeg classification
UR - http://hdl.handle.net/10044/1/90332
ER -