# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
### Related Work  
The use of Event-Related Potentials (ERP) is a common practice in the development of BCI systems; however, their use depends on the mental task to be interpreted. ERPs are brain measurements captured after a cognitive, motor, or sensory event has occurred. Some cognitive functions in humans can be detected after some milliseconds in response to a particular stimulus, which is where ERP-based methods can be useful [11]. However, ERPs are not suitable for the problem studied in this work. Certainly, mental states are normally not triggered by any particular stimulus; rather, they are a prolonged condition in brain activity, activated for psychological reasons like mood change, or by physiological conditions like exhaustion. This explains the high complexity of the task studied in this work: A person could simultaneously be doing multiple activities, producing very complex patterns in the brain, making it difficult to distinguish between both mental states. Literature states that the most promising EEG-based systems have to contain at least the following components: signal pre-processing, feature extraction/selection, and classification [12]. Moreover, hybrid systems are usually the ones with the best performance, making it difficult to classify them into well-defined categories [13]. Because literature related to EEG classification can be overwhelming, we are going to discuss here only the works that try to classify some kind of mental state rather than a particular mental task (e.g., motor imagery). The majority of the research in this area has focused on techniques from signal processing, supervised/unsupervised learning, statistical learning, or a hybridization of these fields, either for feature selection, feature extraction, or classification [14]. Particularly for EEG-based feature extraction in terms of mental states, several approaches have been followed. In [15], a Kernel Partial Least Squares (KPLS) algorithm was used as a feature extraction step for alert and fatigued states. Wavelet Transform (WT) coefficients and some measures of approximate coefficients, like Shannon entropy, were used in [16] for feature extraction, applied to a workload mental states classification problem. [PERSON] et al. [17] also used the WT for feature extraction in drowsiness detection. In these methods, coefficients from the WT decomposition were used as a spectral granularity measure of the signals, suitable as feature vectors. [PERSON] et al. [18] propose the Stockwell transform as the analysis method for feature extraction and use several classifiers to test the system's performance. Based on a valence/arousal framework, general mental states were identified by using the Power Spectral Density (PSD) in the work by [PERSON] et al. [19], focusing on feature extraction rather than classification. Other common approaches to construct new features include the Common Spatial Pattern (CSP), Source Power Co-Modulation (SPoC), and Spatio-Spectral Decomposition (SSD), which were presented in the work by [PERSON] et al. [20]. More advanced CSP approaches have been developed over time to improve the generalization properties [21; 22]. Deep Learning (DL)-based methodologies have been recently used successfully in this type of problem. For example, the work by [PERSON] et al. [23] presents different variants of Deep Neural Networks (DNNs), like Deep Belief Network (DBN), Convolutional Neural Networks (CNNs), and some specific channel-wise CNNs (CCNN). By stripping the label layer, the DNNs were effectively used as feature extractor elements. In terms of feature selection, the number of works is scarce. For example, in [24], the authors used Principal Component Analysis (PCA) to extract a feature vector subset. This is opposed to what happens in mental task research, where there is a high occurrence of feature selection methods, with those being filter- or wrapper-based [14].