PEML-E: EEG eye state classification using ensembles and machine learning methods

Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, etc. This issue is known as electroencephalogram signals. There are many works in this context in which traditional techniques and manually extracted features are used. The extraction of effective features and the selection of proper classifiers are challenging issues. In this study, a classification system named PEML-E was proposed in which a different pre-processing stage is used. The ensemble methods in the classification stage are compared to the base classifiers and the most important works in this context. To evaluate, a freely available public EEG eye state dataset from UCI is applied. The highest accuracy, precision, recall, F۱, specificity, and sensitivity are obtained ۹۵.۸۸, ۹۵.۳۹, ۹۶.۲۵, ۹۶.۱۸, ۹۶.۲۵, and ۹۵.۴۴%, respectively.