A Systematic mapping of feature extraction and feature selection methods of EEG signals for neurological diseases diagnostic



Diagnosis, EEG, electroencephalogram, feature extraction, feature selection, neural disease


EEG signal analysis has several applications in the medical field. It is widely used for clinical diagnostics and for advances in the Brain-Computer Interface (BCI) area. In recent years, several studies about the automatic execution of this analysis have been proposed, motivated by the fact that the traditional activity demands a long time from an expert, besides being subject to a misdiagnosis. Although some proposed methods have good accuracy, many of them are not suitable for an online application due to their high computational cost. Thus, there is opportunity in the area to study the influence of these techniques on diagnostic performance and to propose the optimization of this task. In this context, this Systematic Mapping identifies and evaluates primary studies that present techniques for feature extraction (FE) and selection (FS) to provide automatic diagnosis of a neurological disorder using a classifier.


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