## 1 Introduction  
### Machine Learning and EEG  
The development of techniques that facilitate human behavior analysis has been progressed notably in the last decade, which has driven the development of machine learning tools and signal processing technology that can perform complex analyses of our environments. This has led to a diversification of analysis techniques and even the development of hardware to analyze our environmental signals, such as intelligent voice assistants and wearable devices that can give a detailed follow-up of our vital signs. Similarly, this development has allowed certain fields, such as the analysis of electroencephalography (EEG) signals, to also notably develop in areas that have previously been restricted by costs and technological limitations.  
EEG signal analysis is a non-invasive technique that is widely used to analyze brain activity. Traditionally, the analysis of these signals has focused on the diagnosis and monitoring of medical conditions [1]. However, the interest in this signal has diversified, and machine learning (ML) has played a significant role in EEG signal processing and analysis because it has allowed the development of analysis techniques that successfully perform recognition and classification tasks. Promising results have been achieved in fields as varied as the analysis of mental illness, object manipulation, and cognitive processes [2, 3, 4, 5, 6].  
A central aspect for the study of EEG signals is the premise of obtaining information directly from the cognitive processes of a person and implementing ML to process traits that allow us to identify, analyze, associate, and even control these processes through cognitive responses [3; 7; 8]. When analyzing the EEG signals, it is expected that the data are closely related to the phenomenon and cannot be manipulated. However, it is also a technique that is highly susceptible to experimental noise, so signal processing methodologies need to be carefully established.  
The novelty of our proposal lies in applying the ML tool M3 GP to the conversion of the search space through selecting, combining, deleting, and constructing the most suitable features to ease the classification process of the learning method. This process allows us to improve the classification stage without changing the architectures, which are often obtained through extensive processes.  
The literature shows that the community has proposed diverse EEG data processing architectures; some use large and complex neural networks since their features require it [9] and others argue that low complexity architectures could handle the problem but use distinct spectral feature extractions [10; 11]. The proposed methodology enables the improvement of the recognition rate for this type of analysis without changing the topology.  
Several techniques have been developed for signal processing and feature selection, which have been traditionally based on spectral components [12], since they reduce the computational load for ML processes [11]. Recent literature suggests that automatic feature selection can be achieved using ML algorithms, thereby reducing the signal processing stage and allowing an ML strategy to choose the best-fit features [13; 14]. This proposal is based on implementing MP3G to transform and select the most suitable features by considering a spectral stage instead of performing a greedy search.  
This work focuses on the BED dataset [15], which was developed to evoke and register emotional stimuli using low cost devices that can help to generalize the results of this type of research and also proposes precise experimentation strategies and performs a rigorous signal treatment process. State-of-the-art (SOA) works, as reported in [16], have demonstrated the viability and importance of implementing new and accessible methodologies that seek the greater applicability of this type of research.