# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
### Related Work
Regarding the classifier, the community tends to use a wide range of tools from the machine learning field: Linear Discriminant Analysis (LDA) [9; 20], k-Nearest Neighbors (k-NN) [25], Vector Quantization [26], Support Vector Machine (SVM) [14; 27; 28], Artificial Neural Network (ANN) [16], and bagging-based methods [23], among others. According with the surveyed literature, the majority of works use classical algorithms to solve the problem of mental state recognition; however, alternatives like meta-heuristic methods seem to help when the complexity of the problem is high. From an optimization perspective, algorithms from the Evolutionary Computing (EC) field have been employed as channel reduction or feature selection approaches. Genetic Algorithms (GAs) have been found to be suitable, mostly in wrapper approaches [29; 30; 31; 32]. GAs have also been used to evolve classifiers, like the case of the rule-based Pittsburg-style, where each individual is represented by a variable-length set of rules of the IF-THEN clause type [33]. Other algorithms like Ant Colony Optimization (ACO) [34] or Particle Swarm Optimization (PSO) [35] have also been used. Adaptive approaches have been employed as well, like the auto-reinforced system introduced in [36], by incorporating a fed-back PSO and a rule-based classifier. GP is a particular variant of EC algorithms, with the unique characteristic that solutions are serialized computing elements. An inherent strength of the GP paradigm is its symbolic representation that can be adapted to a wide range of problems. For example, GP can be used either for feature selection, feature extraction, or classification. In [37], a GP multi-tree classifier was used in combination with Empirical Mode Decomposition (EMD) in an EEG dataset for epilepsy detection. A follow-up of this work with modified search operators was presented in [38]. A decision-tree-based model was introduced in [39], where arithmetic and logical rules were inferred in a multi-level GP representation. Although electrocorticogram (ECoG) was used instead of EEG by the authors in [40], GP was employed as a classifier to solve an epileptic seizure recognition problem. In [41], the authors proposed a multi-layered population GP where each layer evolves a discriminant function upon a training set; consequent layers evolve on top of the previous, improving the overall classification accuracy. A learning assembly was evolved by GP to build discriminant functions in the work by [PERSON] et al. [42]. More closely related to this work, [PERSON] et al. [25] built a feature extraction model using GP for an epilepsy EEG dataset. In [43], GP was used similarly, as this was a feature extraction task. These cited works are further discussed in Section 3.1, since there are some differences worth reviewing compared with our proposal. Apart from feature extraction methods, GP can be used as a simple feature selection tool like in [44]. In [45], although it is not applied to EEG, GP was used to build feature transformation models where the individual is represented with a single root node, thus producing a single new feature. The remainder of this paper is organized as follows. In Section 2, the reference system based on CSP and LDA is described and analyzed. In Section 3, we discuss the proposed system in detail, particularly the GP-based feature extraction method called \({}^{+}\)FEGP. The experimentation and results are presented in Section 4. In Section 5, the experimental results are discussed. Finally, in Section 6, we present our conclusions and future work.