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Abstract
A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets, Daubechies and Symlets. Euclidean distance between clusters normalized with respect to the standard deviation of the whole set of data are used to separate each task performed by participants. The results of this stage allow for an assessment of separability between subsets of data associated with each activity performed by experiment participants. The speed of convergence of the training process employing deep learning-based clustering is also measured.
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3233/FI-2019-1831
- License
- Copyright (2019 IOS Press)
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
FUNDAMENTA INFORMATICAE
no. 168,
pages 249 - 268,
ISSN: 0169-2968 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Kurowski A., Mrozik K., Kostek B., Czyżewski A.: Method for Clustering of Brain Activity Data Derived from EEG Signals// FUNDAMENTA INFORMATICAE -Vol. 168,iss. 2-4 (2019), s.249-268
- DOI:
- Digital Object Identifier (open in new tab) 10.3233/fi-2019-1831
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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