Neuronal Spectral Analysis of EEG and Expert Knowledge Integration for
  Automatic Classification of Sleep Stages
  Being able to analyze and interpret signal coming from electroencephalogram
(EEG) recording can be of high interest for many applications including medical
diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able
to extract from this signal many hints related to physiological as well as
cognitive states of the recorded subject and it would be very interesting to
perform such task automatically but today no completely automatic system
exists. In previous studies, we have compared human expertise and automatic
processing tools, including artificial neural networks (ANN), to better
understand the competences of each and determine which are the difficult
aspects to integrate in a fully automatic system. In this paper, we bring more
elements to that study in reporting the main results of a practical experiment
which was carried out in an hospital for sleep pathology study. An EEG
recording was studied and labeled by a human expert and an ANN. We describe
here the characteristics of the experiment, both human and neuronal procedure
of analysis, compare their performances and point out the main limitations
which arise from this study.
