Neural Network Analysis of the Mastoid EEG for the Assessment of Vigilance

Abstract

This article is concerned with the analysis of the mastoid electroencephalogram (EEG) using parametric modeling and neural network techniques to assess the vigilance of an individual. One possible application of this work would be the design of a monitoring system for tracking the transitions within the vigilance continuum.

The strategy presented consists of training neural networks with spectral features extracted from the mastoid EEG. The results are validated against the expert scoring of the vigilance level performed by visual inspection of the central EEG, electrooculographic (EOG), and electromyography (EMG) signals, and against the results obtained by training similar networks with information extracted from the central EEG (widely recognized as providing useful information for vigilance level assessment). To improve the performance of the neural networks, a Kohonen map-based technique for filtering the training data is proposed (this allows labels assigned by an expert to 15-sec epochs to be transcripted reliably to 1-sec segments). The results presented demonstrate conclusively that the tracking of fluctuations from alertness to drowsiness within the vigilance continuum is possible by neural network analysis of a single channel of EEG recorded from the mastoid site.