# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
###### Abstract  
Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vector Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach's features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature. E 2021 22 July 2021  
data mining is to train the models with relevant features, that is, those features that do not represent noise for the learning model and, on the contrary, have a high power of differentiation between classes. On the other hand, feature selection (FS) helps build robust classification models [8] by identifying relevant features. This process is a mandatory task, especially when the datasets have (i) high dimensionality [7] or (ii) more features than instances, which means the dataset has more columns than rows. This scenario coincides with the classification of abnormalities in EEGs considering the large number of feature extractors reported in the literature and the low availability of datasets with instances or single rows that describe epileptiform events. Besides, the literature review shows the use of different feature selection techniques to support the automatic analysis of different types of physiological signals. For example, proposals range from general methods to select features on clinical databases [9] to implementations designed to help the diagnosis of diseases such as Alzheimer's [10], multiple sclerosis [11], sleep disorders [12], and epilepsy [13]. Additionally, the list of reviewed papers presents solutions designed for the detection of emotions by analyzing the electrical activity of the brain [14] or the recognition of activities using analysis of physiological signals [15] and external devices [16]. Furthermore, some literature has reported feature selection methods to identify features with the more remarkable power of differentiation in classifying or detecting epileptic patterns. However, most of the reviewed results focused on identifying specific patterns using a set of features without considering each feature's relevance or impact in subsequent analyses [6]. Thus, the proposals end up training machine learning models with features that could represent noise or redundancy for the learning process. Recently, several studies have focused on improving the performance of feature selection algorithms. For example, in [17], the authors proposed identifying correlations between features and classes to enhance the effectiveness and maintain a low computational cost in the feature selection process. Additionally, Refs. [18; 19] incorporated techniques such as bootstrap to select features using samples from the original dataset and integrate the subsets of features generated.