Recent research has shown that brain waves contain useful information about intention or mind. After some training sessions, distinctive patterns associated with specific intentions can be produced and detected from brain waves, which can be used to generate commands to control computers and robots. One of the interesting applications of this idea is prosthesis control for the disabled. We are developing novel methods for brain wave pattern detection and analysis (feature extraction, selection, and classification), and online BCI systems for specific applications with various BCI protocols.
A. Developing
Reliable Methods for Onset Detection in Self-paced Brain Computer Interface
Systems
Self-paced brain computer interface (SBCI) systems allow people
with motor disabilities to use their brain signals to control devices, whenever
they wish, which would play an important role in improving independence and
quality of life of disabled people. One of the major challenges in SBCI is to
detect the onset of the user’s intentional control accurately and reliably.
This research aims to develop novel robust methods for onset detection for
motor imagery based SBCI systems. New effective features for onset detection
will be explored from different perspectives. Probabilistic models and machine
learning approaches will be developed for onset detection after feature
extraction. The newly developed methods will be tested on both offline EEG data
and online SBCI systems with a properly selected application.
B. SSVEP-based
Emotion Related Attention Bias Detection and Modification
Mental disorders have greatly affected the quality of life of many people around the world. Some mental disorders are due to attention bias, such as stress and anxiety due to attention bias to negative emotions. Unlike the traditional psychological approach to attention bias detection and modification, this PhD work will deal with the problem through steady-state visually evoked potential (SSVEP) analysis. Both positively and negatively emotional stimuli (images or videos), suitable for inducing SSVEP, will be designed and extensive SSVEP experiments using subjects with low-level and high-level anxieties will be conducted. Novel methods for effective feature extraction and selection from SSVEP will be developed. From these features effective biomarkers for attention bias detection will be identified using machine learning and data mining methods. Another important part of this PhD work is to interpret the identified biomarkers from the perspective of neuroscience and make use of them to develop online SSVEP-based biofeedback for attention bias modification or intervention.
Please find more information from http://dces.essex.ac.uk/staff/jqgan/gan.htm, including funded projects and demo videos.
More general research interests related to this topic include data mining, feature extraction and selection, high-dimensional data modelling, clustering, and classification, with applications in biomedicine, financial engineering, and other fields.
With the ever-increasing quantity of electronic data available from complex industrial and commercial processes and systems whose first-principle models are unknown, it is highly desirable to construct data-driven models that have the capability to interpret data and to predict the future behaviours of these processes and systems. One challenging problem occurs in this area when the input space dimension of the system to be modelled is high. In this situation, it is extremely difficult to model the complex system accurately using a single global model, and it is also very challenging to use local modelling approaches, such as advanced neurofuzzy modelling techniques, due to the "curse of dimensionality problem". Another interesting and challenging problem with local modelling is how to achieve not only a good global approximation but also close approximations of local behaviours of the nonlinear system by local models, as this requirement will enhance the interpretability and applicability of the local models. We are investigating criteria for model interpretability, and developing new local modelling techniques including machine learning algorithms and the relevant theory for generating interpretable models from data.
Being able to understand the environment (usually time-varying and unknown a priori) is an essential prerequisite for intelligent/autonomous systems such as intelligent mobile robots. The environmental information can be acquired through various sensors, but the raw information from sensors are often noisy, imprecise, incomplete, and even superficial. To obtain from raw sensory data an accurate internal representation of the environment, or a digital map with accurate positions, headings, identities of the objects in the environment, is very critical but very difficult in the development of robotic systems. The major challenge is due to the uncertainty of the environment and the insufficiency of sensors. Basically there are two categories of techniques for handling uncertainties: adaptive and robust. Adaptive techniques exploit a posteriori uncertainty information that is "learnt" on-line, whilst robust techniques take advantage of a priori knowledge about the environment and sensors. We are investigating novel computer vision methods including invariant feature extraction, multisensor data fusion methods, and multiple model approaches.
Please find more information from http://dces.essex.ac.uk/staff/jqgan/gan.htm, including funded projects and demo videos.
(4) Pattern Recognition and Machine Learning Approach to Web Information Retrieval and Mining
Developing methods for effective and efficient retrieval and analysis of web information, including multimedia, has posed significant challenges in recent years to researchers in a wide range of areas including computational intelligence. The challenges are mainly due to the fact that the Web with diverse information is massive, dynamic and distributed. This research aims to develop novel methods for both web information retrieval and mining, including effective methods for obtaining relevant information from the Web and pattern recognition and machine learning based methods for web information analysis such as novel feature extraction, classification and clustering methods.
For more information, please contact Professor Gan. Prospective students are welcome to discuss their own ideas with Professor Gan.
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This page was last modified by John Gan in May 2012
E-mail: jqgan @essex.ac.uk