Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44277
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
Classification
Electroencephalogram
Emotion recognition
Issue Date: 2017
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.
URI: http://hdl.handle.net/10553/44277
ISBN: 978-3-319-70095-3
ISSN: 0302-9743
DOI: 10.1007/978-3-319-70096-0_73
Source: Liu D., Xie S., Li Y., Zhao D., El-Alfy ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science, vol 10635. Springer, Cham
Appears in Collections:Actas de congresos
Show full item record

SCOPUSTM   
Citations

5
checked on Aug 2, 2020

Page view(s)

26
checked on Aug 1, 2020

Google ScholarTM

Check

Altmetric


Share



Export metadata



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