Information Technology Reference
In-Depth Information
9
Feature Extraction and Classification
of EEG Signals. The Use of a Genetic
Algorithm for an Application
on Alertness Prediction
Pierrick Legrand, Laurent V´zard, Marie Chavent,
Fr ´d´ rique Fa¨ta-A¨nseba and Leonardo Trujillo
Abstract
This chapter presents a method to automatically determine the alertness state of
humans. Such a task is relevant in diverse domains, where a person is expected
or required to be in a particular state of alertness. For instance, pilots, security
personnel, or medical personnel are expected to be in a highly alert state, and this
method could help to con
rm this or detect possible problems. In this work,
electroencephalographic (EEG) data from 58 subjects in two distinct vigilance
states (state of high and low alertness) was collected via a cap with 58 electrodes.
Thus, a binary classi
cation problem is considered. To apply the proposed
approach in a real-world scenario, it is necessary to build a prediction method
that requires only a small number of sensors (electrodes), minimizing the total
cost and maintenance of the system while also reducing the time required to
