A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals
Journal of Medical Systems
Emotion and Attention Interaction Studied through Event-Related Potentials
Journal of Cognitive Neuroscience
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.