# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 6 Conclusions and Future Work
most important blocks in these types of systems is the feature extraction process, where the pattern recognition of the underlying data is performed. Evidence found during experimentation allowed us to provide some insights in the following directions. First, the \({}^{+}\)FEGP algorithm in a wrapper scheme produced improved performance compared to the system with just CSP, reaching a classification accuracy of 78.8% for unseen data. Certainly, any system could efficiently benefit from the hybridization of different tools to achieve competitive results. A sole technique usually cannot tackle such a complex problem. Second, the proposed genetic operators allow us to evolve transformation models with good testing performance. Third, the unique fitness function steered the search toward solutions that avoided over-fitting in most of the cases. Fourth, the sizes of the evolved solutions are relatively small in terms of the number of new features and the number of nodes per subtree, resulting in faster execution times during the testing phase. Moreover, in our tests, \({}^{+}\)FEGP was not affected by bloat [63]. Fifth, although from the system design point of view, the classifier can be interchanged, other classifiers did not produce better results compared with LDA. Nevertheless, when examining the whole scenario, we can state that most of the shortcomings were derived from the underlying data properties. In this case, we could opt for three options--increasing the system's complexity by incorporating an additional processing step and helping reduce further issues like over-fitting, using adaptive classifiers, or increasing the number of EEG recordings. In all of the circumstances, we can foresee applications of the \({}^{+}\)FEGP outside of this domain, that is, by merely adjusting the fitness function, it can be used in any data transformation scenario. There are several aspects to consider in future work. One is the introduction of feature selection into the system; ultimately, channel reduction is very important for any real-world scenario, mainly for practical reasons. It is also of our interest to increase the number of mental states in future research. Although we emphasize the benefits of the proposed \({}^{+}\)FEGP algorithm in this work, the exploration of different tools in pre-processing and post-processing is also important. Moreover, we do not express by any means that the \({}^{+}\)FEGP algorithm in conjunction with CSP is the best option, but it is rather a choice that proved to be very competitive in this domain. It is well researched that simple algorithms like CSP are not capable of performing well with complex brain tasks, and there is a limit where the balance between simplicity and practicality ends. As the computer power increases, the need for simpler methods is not necessarily a wanted goal if the results do not improve. The incorporation of GP into the development of efficient BCI systems can be seen as a viable direction in terms of capacity to find new patterns hidden in EEG data. Although GP, like many other machine learning algorithms, has a costly training phase, the testing phase is rather simpler and more efficient, making it quite useful for BCI systems. Another aspect is the improvement of the \({}^{+}\)FEGP algorithm itself. Further investigation is required to study the evolution behavior; there are still open questions: What is the relationship between the number of features and the efficiency of individual subtrees? Is there a way to fully avoid bloat in longer searches? Are there better genetic operators that allow one to search more efficiently? In our previous works, by implementing a type of local search into GP, we achieved better results in regression [65] and classification problems [66], suggesting that GP generally benefits from the hybridization of search operators. This encourages us to extend these approaches into this domain.