Abstract
The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.
Highlights
There are human activities that are considered to have a high risk because they require a response within a given time window with an associated cost
We propose a new methodology for the automatic classification of two mental states, involving a novel algorithm for feature extraction based on the Genetic Programming (GP) paradigm
Given the stochastic nature of GP, a series of multiple runs were executed in order to statistically determine the algorithm performance
Summary
There are human activities that are considered to have a high risk because they require a response within a given time window with an associated cost. Some of the causes of unintentionally low states of vigilance are associated with sleep deprivation [1], monotonous tasks [2], or stress [3]. Human consciousness has two main components: wakefulness and awareness of the environment [4]. Wakefulness is associated with the content of consciousness and awareness with the level of consciousness. These two, in the majority of situations, are heavily correlated with normal physiological states (with the exception of dream activity during Rapid Eye Movement (REM) sleep) [5]
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