1. BCI Research at the ISRC, University of Ulster N. Ireland, UKBy
Dr. Girijesh Prasad
Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Engineering, University of Ulster, Magee Campus, N. Ireland, UK
ISRC URL:

2. Presentation outline RTD facilities BCI research expertise
Ongoing research work
Project Proposal

3. RTD Facilities
We are part of a recently launched £20M centre of excellence in Intelligent Systems with core activities in:
Software and hardware implementations of neural networks, fuzzy systems, Gas
Bio-inspired Intelligent Systems – Brain Modeling
Brain computer Interfacing (BCI)
Intelligent Robotics
Embedded systems
Re-configurable computing/FPGA systems
Multiple valued logic systems
Machine vision
Intelligent process control
BCI laboratory facilities:
A state-of-art BCI experimental setup with 56 EEG channels, 8 EMG channels
An 8 EEG/EMG channel mobile unit
A 4 EEG channel wireless mobile unit
EMF screened room
Multiple electrode systems

4. Expertise Main BCI research objectives:
To devise a robust BCI system that can self-organise and adapt to each individual’s EEG autonomously and to the inherent day-to-day changes
To effectively account for uncertainties inherently present in EEG due to non-stationary brain dynamics and varying noise characteristics of the measurement environment
RTD work Undertaken and ongoing :
Time-series prediction approach for pre-processing and feature extraction
Comprehensive and conclusive analysis of best spectral approaches to feature extraction
Type-2 fuzzy logic (T2FL) approach for classifier design
Design of neurofeedback
A self-organising non-parametric BCI
Real-time implementation and extensive experimental evaluation on more than 14 subjects over several months

5. Type-2 Fuzzy Logic-based Methodology
Expertise …cont.
EEG trial C3
Feature Extraction
C3 feature vector
C4 feature vector C4
T2 FL-based
Classifier
Class label IF
THEN
LEFT or RIGHT
hand imagery
Type-2 Fuzzy Logic-based Methodology
Promising results: Significantly higher robustness and classification accuracy

6. Expertise …cont. Ongoing research work
Advanced modelling approaches for improving feature separability
Enhancing mental practice through BCI-based neurofeedback for post stroke rehabilitation
Self-adaptive Asynchronous Brain-Computer Interface
Participation in large-scale national and EU projects
e.g. SenseMaker - a multi-sensory, task-specific adaptable perception system, (IST ) Funded under EUFP5 on Life-like Perception, (0.5M Euros),

7. An Intelligent System for Post-stroke Rehabilitation: A project proposal
An EEG-based brain-computer interface for neurofeedback.
Development of an appropriate neurofeedback to motivate subjects for focussed mental practice of rehabilitation exercises over long-term.
Industrial involvement for prototype product development.
Incorporation of a robotic system that facilitates physical exercises in case of extreme disability.
Involvement of health specialists such as neuro-psychologists.
Provision for modifications of therapeutic strategy dependent on intelligent sensory data (e.g. EMG and EEG) analysis, and human responses.

8. Project consortia Partners are sought with relevant expertise.
We are also interested in joining other consortia for:
BCI Research and Development
Advanced Intelligent Systems for assistive technology
Computational Intelligence and cognitive Systems

9. Conclusions
Robustness, uncertainty handling, and adaptability are key challenges.
Our TSP approach as an EEG data preprocessing procedure significantly enhances effectiveness of feature extraction procedures.
T2FLS based classification approach shows potential in more effective handling of signal variability and uncertainty.
Further algorithmic enhancement is needed for developing a practical BCI. Planned ongoing work is addressing this.
Project partners are sought.

10. Questions
Thank you