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.


BibTex format

author = {Sun, Y and Lo, FPW and Lo, B},
doi = {10.1016/j.eswa.2019.01.080},
journal = {Expert Systems with Applications},
pages = {259--267},
title = {EEG-based user identification system using 1D-convolutional long short-term memory neural networks},
url = {http://dx.doi.org/10.1016/j.eswa.2019.01.080},
volume = {125},
year = {2019}

RIS format (EndNote, RefMan)

AB - Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.
AU - Sun,Y
AU - Lo,B
DO - 10.1016/j.eswa.2019.01.080
EP - 267
PY - 2019///
SN - 0957-4174
SP - 259
TI - EEG-based user identification system using 1D-convolutional long short-term memory neural networks
T2 - Expert Systems with Applications
UR - http://dx.doi.org/10.1016/j.eswa.2019.01.080
UR - http://hdl.handle.net/10044/1/75200
VL - 125
ER -