Group Members

Please add to the e-mail addresses shown under the names below


Permanent Academics  

  Senior Researchers

PhD Students  

  Former Members

Permanent Academics

Prof. John Q.





Dr Luca





Prof. Riccardo



Dr. Francisco Sepulveda



Senior Researchers

Dr. Caterina





Dr. Mathew






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Ph.D. Students

(in alphabetical order)



Luz Maria Alonso V.:   Project: A Virtual Environment Platform for Simulated BCI-Enabled Independent Living

The aim of this project is to design and develop a Virtual Environment (VE), involving auditory and visual systems, in order to research several mental patterns which are elicited by the interaction between the user and the devices into the VE. The focus will be on brain control of the environment and on the effects this interaction will have on the brain signals.  The platform will allow for more realistic testing of BCI methods for independent living for disabled individuals.

Tugce Balli:   Project: Non Linear Methods for Biomedical Time Series Analysis

Linear modelling techniques have been widely used in the literature for analysis of biological time series (e.g., EEG) as they are simpler in principle compared to their non-linear counterpart. However conventionally used linear modelling techniques would fail to capture the nonlinearities in time series. It is a known fact that the individual neurons in the brain behave in a nonlinear way thus we can expect a huge network of those individual neurons to do so. Because of that, there has been an increasing interest in using nonlinear methods for analysis of biological time series. In this research we set out to investigate nonlinear modelling and feature extraction techniques for the analysis of biological signals.

Navin Cota:    Project: High Security Authentication Using Brain's Electrical Response Patterns

Automatic verification is very important in ensuring security for access to restricted areas (like control gates and military applications). My proposed doctoral research will authenticate users using their brain signals in response to a sequence of on screen stimuli (say pictures). The advantage of using such brain electrical activity as biometric is its fraud resistance. Objectives for the proposed research work include developing suitable paradigms and analysis frameworks for an online BCI based biometric system.

Mathew Dyson:  Project: Mental Tasks and Location Selection for Asynchronous BCIs





Bashar Awwad Shiekh Hasan:  Project:  Adaptive Asynchronous BCIs

The goal of this project is to develop probabilistic classification models to better handle the modelling of temporal and spatial information of EEG data within self-paced BCI settings. Adaptive algorithms of these models will also be developed to handle the non-stationarity problem of EEG data in general and especially for motor-imagery self-paced BCI. Intelligent user interfaces will be investigated to better obtain online labels that can help the BCI system to adapt better to the ongoing data.



Badr Hubais:   Project: BCI control of Robotic Arm

The main objective this project is to build a system that can translate brain activity through EEG to direct commands for a robotic arm. The commands the systems attempts to translate are the EEG of wrist movement and "imaginary" wrist movement.


C. S. Louis Tsui:  Project: Adaptive Asynchronous Brain-Actuated Robotic Control

My project aims to develop a motor imagery based adaptive asynchronous BCI for robotic control. Although self-paced BCIs offer more natural human machine interaction, the lack of indications of the user's control intentions and timing brings about challenges in design and performance evaluation. A new asynchronous BCI paradigm has been designed for providing information about class labels (user's control intention and timing) during online experiments, so that online training and adaptation of self-paced BCI can be effectively investigated.

John Wilson    Project: Visual and Audio Evoked Potentials for BCIs

Using Visual (SSVEP) and Audio (ASSR) Steady State Evoked Potentials to build a viable Brain Computer Interface. By tagging stimuli with distinct temporal frequencies it is possible to determine the attention of a subject through spectral analysis of a recorded EEG signal. A subject can then make choices by simply focusing or listening to the stimuli they desire. 



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Former Group Members

  • Dr. Ramaswami Palaniappan, 2006-2012, now at the University of Kent

  • Dr. Tao  Geng, post-doctoral researcher from 2006 to 2008

  • Dr. Yusuf Khan, visiting researcher from Oct. 2007 to April 2008

  • Dr. Aleksandra Vuckovic, post-doctoral researcher until Aug., 2007

  • Cristian Nikolajsen and Esben Wermuth, from Aalborg University, visiting MSc students in the Autumn 2007

  • Dr. Heba Lakany, lecturer until Aug., 2007

  • Dr. Shang-Ming Zhang, PhD student until 2006

  • Israel Navarro, PhD student in 2004/2005


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Page last modified by Francisco Sepulveda

(E-mail: fsepulv) on March. 12, 2009