Intelligent enhancement and interpretation of EEG signals

Describes the development of a technique which identifies frequency clusters within an EEG and calculates for each cluster the following features: amount, organisation, frequency, amplitude, location, symmetry and changes on eye opening. Previously developed techniques to interpret the EEG represent expertise using conventional knowledge-based systems and algorithms. These systems produce discontinuous outputs, switching from one deduction to another when the inputs cross rule boundaries. The technique described in this paper provides more accurate approximation to the expertise by basing the representation of the knowledge and inference on fuzzy sets and fuzzy logic. In such systems, boundaries need not exist. Only at the final stage-linguistic approximation-do any effects of boundaries have any effect, and these can be minimised by appropriate selection of primary fuzzy sets, hedges and connectives. Previous techniques neither take into account the effect of artefacts or adequately model the expertise, which is widely recognised as largely subjective. This paper brings together work carried out in both these areas to produce a system which should provide value to the clinical workplace. The system proposed eliminates the bias in the output by omitting from the clustering procedure frequency peaks which are suspected of having artefact origin by incorporating work from Wu et al. (1994).