Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing

Highlights? A wrapper method is suggested to get discriminant features in neuroscience. ? SVM-RFE is proposed for feature selection related to affective valence. ? Topography-time-frequency analysis is used to get relevant scalp regions. ? Morlet wavelet filter is used for feature extraction from event related potentials. In this work, event related potentials (ERPs) induced by visual stimuli categorized with different value of affective valence are studied. EEG signals are recorded during visualization of selected pictures belonging to International Affective Picture System (IAPS). A Morlet wavelet filter is used to transform the EEG input space to a topography-time-frequency feature space. Support vector machine-recursive feature elimination (SVM-RFE) is applied for detecting scalp spectral dynamics of interest (SSDOIs) in this feature space, allowing to identify the most relevant time intervals, frequency bands and EEG channels. This feature selection method has proven to outperform the classical t-test in the discrimination of brain cortex regions involved in affective valence processing. Furthermore, the presented combination of feature extraction and selection techniques can be applied as an alternative in other different clinical applications.

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