Journal
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT I
Volume 10245, Issue -, Pages 187-198Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59063-9_17
Keywords
Electroencephalography; Event-related potentials; P300; Brain-computer interface; Stacked autoencoders; Deep learning
Categories
Funding
- UWB [SGS-2016-018]
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Deep learning has emerged as a new branch of machine learning in recent years. Some of the related algorithms have been reported to beat state-of-the-art approaches in many applications. The main aim of this paper is to verify one of the deep learning algorithms, specifically a stacked autoencoder, to detect the P300 component. This component, as a specific brain response, is widely used in the systems based on brain-computer interface. A simple brain-computer interface experiment more than 200 school-age participants was performed to obtain large datasets containing the P300 component. After feature extraction the collected data were split into the training and testing sets. State-of-the art BCI classifiers (such as LDA, SVM, or Bayesian LDA) were applied to the data and then compared with the results of stacked autoencoders.
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