Unsupervised EEG feature extraction based on echo state network
| Autor: | Jianing Tong, Leilei Sun, Hui Xiong, Chuanren Liu, Haoyu Yang, Bo Jin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Computer science Feature extraction 02 engineering and technology Electroencephalography Theoretical Computer Science Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Cluster analysis medicine.diagnostic_test business.industry 05 social sciences 050301 education Pattern recognition Autoencoder Computer Science Applications Autoregressive model Control and Systems Engineering Pattern recognition (psychology) 020201 artificial intelligence & image processing Artificial intelligence Echo state network business 0503 education Software |
| Zdroj: | Information Sciences. 475:1-17 |
| ISSN: | 0020-0255 |
| Popis: | Advanced analytics such as event detection, pattern recognition, clustering, and classification with electroencephalogram (EEG) data often rely on extracted EEG features. Most of the existing EEG feature extraction approaches are hand-designed with expert knowledge or prior assumptions, which may lead to inferior analytical performances. In this paper, we develop a fully data-driven EEG feature extraction method by applying recurrent autoencoders on multivariate EEG signals. We use an Echo State Network (ESN) to encode EEG signals to EEG features, and then decode them to recover the original EEG signals. Therefore, we name our method feature extraction based on echo state network, or simply FE-ESN. We show that the well-known autoregression-based EEG feature extraction can be seen as a simplified variation of our FE-ESN method. We have conducted experiments on real-world EEG data to evaluate the effectiveness of FE-ESN for both classification tasks and clustering tasks. Experimental results demonstrate the superiority of FE-ESN over the state-of-the-art methods. This paper not only provides a novel EEG feature extraction method but also opens up a new way towards unsupervised EEG feature design. |
| Databáze: | OpenAIRE |
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