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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2015

01.04.2015 | Advances in Intelligent Data Processing and Analysis

Learning to decode human emotions from event-related potentials

verfasst von: O. Georgieva, S. Milanov, P. Georgieva, I. M. Santos, A. T. Pereira, C. F. Silva

Erschienen in: Neural Computing and Applications | Ausgabe 3/2015

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Abstract

Reported works on electroencephalogram (EEG)-based emotion recognition systems generally employ the principles of supervised learning to build subject-dependent (single/intra-subject) models. Building subject-independent (multiple/inter-subject) models is a harder problem due to the EEG data variability between subjects. The contribution of this paper is twofold. First, we provide a framework for selection of a small number of basic temporal features, event-related potential (ERP) amplitudes, and latencies that are sufficiently robust to discriminate emotion states across multiple subjects. Second, we test comparatively the feasibility of six standard unsupervised (clustering) techniques to build intra-subject and inter-subject models to discriminate emotion valence in the ERPs collected while subjects were viewing high arousal images with positive or negative emotional content.
Vorheriger Artikel NECM: Neutrosophic evidential c-means clustering algorithm
Nächster Artikel Large, high-dimensional, heterogeneous multi-sensor data analysis approach for process yield optimization in polymer film industry

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Metadaten
Titel
Learning to decode human emotions from event-related potentials
verfasst von
O. Georgieva
S. Milanov
P. Georgieva
I. M. Santos
A. T. Pereira
C. F. Silva
Publikationsdatum
01.04.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1653-6

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