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2017 | OriginalPaper | Buchkapitel

Optimized Echo State Network with Intrinsic Plasticity for EEG-Based Emotion Recognition

verfasst von : Rahma Fourati, Boudour Ammar, Chaouki Aouiti, Javier Sanchez-Medina, Adel M. Alimi

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Task-Free Brainprint Recognition Based on Degree of Brain Networks

Nächstes Kapitel A Computational Investigation of an Active Region in Brain Network Based on Stimulations with Near-Infrared Spectroscopy

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Titel: Optimized Echo State Network with Intrinsic Plasticity for EEG-Based Emotion Recognition
verfasst von: Rahma Fourati
Boudour Ammar
Chaouki Aouiti
Javier Sanchez-Medina
Adel M. Alimi
Verlag: Springer International Publishing
Buch: Neural Information Processing
Print ISBN: 978-3-319-70095-3

Electronic ISBN: 978-3-319-70096-0

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70096-0_73

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