No subject

three distinct patterns which roughly corresponded to the global brain
states W (wakefulness), S (slow wave or deep sleep) and R (REM sleep). The
outputs from the self-organizing feature map were subsequently mapped via a
Radial Basis Function (RBF) classifier onto three outputs, trained by
sections of data on which experts agreed upon the stage W, S, or R. For
each input, the resulting network produced probabilities for the three
global stages, describing intermediate stages as a combination of three
'mixing fractions' .

This general approach, yielding a novel way of describing brain state while
exploiting some of experts' knowledge in a partly supervised method, will
be adopted and extended in the following ways

  Many features extracted from the signals will be considered.
 
  Instead of the 2-dimensional feature map used by R&T, alternative
approaches will be investigated.  It has been shown that combinations of
other clustering and mapping methods can outperform SOMs. Moreover, since
topographic mapping is only exploited for visualization, the general
approach can be based on more advanced clustering techniques (e.g.
techniques for non-Gaussian clustering (Roberts 1997) or Bayesian-inspired
methods).
 
  in order to cope with the large number of input features to be
investigated active feature selection methods will be applied.
 
  techniques for intelligent sensor fusion will be investigated. When
multiple sources are combined to lead to classification results, it is not
trivial to decide which are the most relevant sources at any given time, or
what should happen when sources fail to provide input (e.g. because an
electrode is faulty). Approaches based on the computation of running error
measures can be employed here.

The Imperial College group will form be a leading centre in the theory
subgroup of the project.  We will be active in the researching of

  Mixture density networks and mixtures of experts
  Model estimation and pre-processing
  Active sensor fusion
  Active feature and data selection
  Unsupervised data partitioning methods (clustering)
  Model comparison and validation


References

1. S.J. Roberts, Parametric and Non-parametric Unsupervised Cluster
Analysis.  Pattern Recognition, 30 (2) ,1997.
 
2. S.J. Roberts and L. Tarassenko, The Analysis of the Sleep EEG using a
Multi- layer Neural Network with Spatial Organisation. IEE Proceedings Part
F, 139(6), 420-425, 1992a.
 
3. S.J. Roberts and L. Tarassenko, New Method of Automated Sleep
Quantification.  Medical and Biological Engineering and Computing, 30(5),
509-517, 1992b.


3) Assessment of cortical vigilance

This project, funded by British Aerospaces Sowerby Research Centre at
Bristol, aims to assess and predict lapses in vigilance in the human brain.
Recordings of the brains electrical activity are to be recorded and
analysed.

The utility of a device or system which may monitor an individual's level
of vigilance is clear in a range of safety-critical environments.  We
propose that such utility would be enhanced by a system which, as well as
monitoring the present state of vigilance, made a prediction as to the
likely evolution of vigilance in the near future.  To perform both these
tasks, i.e. a static pattern assessment and a dynamic tracking and
prediction, sophisticated methods of information extraction, sensor fusion
and classification/regression must be employed.  Over the last decade the
theory of artificial neural' networks has been pitched within the
framework of advanced statistical decision theory and it is within this
framework which we intend to work.

The aim of the project is to work towards a practical real-time system.
The latter should be minimally intrusive and should make predictions of
future vigilance states.  Where appropriate, therefore, the investigation
will assess each technique in the developing system with a view to its
implementation in a real-time environment.

The project will involve research into :

  New methods of signal complexity and synchronisation estimation
  Information flow estimation in multi-channel environments
  Active sensor fusion
  Prediction and classification
  Error estimation
  State transition detection and state sequence modelling

Reference

Makeig, S. and Jung, T-P. and Sejnowski, T. (1996), Using feedforward
neural networks to monitor alertness from changes in EEG correlation and
coherence, Advances in Neural Information Processing Systems (NIPS), MIT
Press, Cambridge, MA.