Please use this identifier to cite or link to this item:
Title: Optimized echo state network with intrinsic plasticity for EEG-based emotion recognition
Authors: Fourati, Rahma
Ammar, Boudour
Aouiti, Chaouki
Sanchez-Medina, Javier 
Alimi, Adel M.
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Echo state network
Intrinsic plasticity
Feature extraction
Electroencephalogram, et al
Issue Date: 2017
Publisher: Springer
Journal: Lecture Notes in Computer Science 
Conference: 24th International Conference on Neural Information Processing, (ICONIP 2017) 
Abstract: Reservoir Computing (RC) is a paradigm for efficient training of Recurrent Neural Networks (RNNs). The Echo State Network (ESN), a type of RC paradigm, has been widely used for time series forecasting. Whereas, few works exist on classification with ESN. In this paper, we shed light on the use of ESN for pattern recognition problem, i.e. emotion recognition from Electroencephalogram (EEG). We show that the reservoir with its recurrence is able to perform the feature extraction step directly from the EEG raw. Such kind of recurrence rich of nonlinearities allows the projection of the input data into a high dimensional state space. It is well known that the ESN fails due to the poor choices of its initialization. Nevertheless, we show that pretraining the ESN with the Intrinsic Plasticity (IP) rule remedies the shortcoming of randomly initialization. To validate our approach, we tested our system on the benchmark DEAP containing EEG signals of 32 subjects and the results were promising.
ISBN: 978-3-319-70095-3
ISSN: 0302-9743
DOI: 10.1007/978-3-319-70096-0_73
Source: Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, v. 10635 LNCS, p. 718-727
Appears in Collections:Capítulo de libro
Show full item record

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.