Identification of sparse spatio-temporal features in Evoked Response Potentials
Résumé
Electroencephalographic Evoked Response Potentials (ERP)s exhibit distinct and individualized spatial and temporal characteristics. Identification of spatio-temporal features improves single-trial classifica- tion performance and allows a better understanding of the underlying physiology. This paper presents a method for analyzing the spatio-temporal characteristics associated with Error related Potentials (ErrP)s. First, a resampling procedure based on Global Field Power (GFP) extracts tem- poral features. Second, a spatially weighted SVM (sw-SVM) is proposed to learn a spatial filter optimizing the classification performance for each temporal feature. Third, the so obtained ensemble of sw-SVM classifiers are combined using a weighted combination of all sw-SVM outputs. Re- sults indicate that inclusion of temporal features provides useful insight regarding the spatio-temporal characteristics of error potentials.
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