Back to Search
Start Over
Unsupervised EEG feature extraction based on echo state network
- Source :
- Information Sciences. 475:1-17
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- 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.
- Subjects :
- 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
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 475
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........59553e8a8dd94e461b72dfa86c44ceba