EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

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.

State-of-the-Art on Brain-Computer Interface Technology

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Variation Trends of Fractal Dimension in Epileptic EEG Signals

“…It is very important to analyze EEG and obtain its implicit correct and important information, because that EEG signals is closely related to brain activity, which could reflect the psychological state, emotional changes, brain activity and the body functionalities, etc. [6]. Therefore, EEG is crucial for understanding the information processing of the human brain [4], enabling the investigations on human brain activity and cognitive processes.…”