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Abstract
A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is shown. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial Neural Networks, Support Vector Machines, Fuzzy or k-Means Clustering. Similarities and differences between all considered methods of an automatic EEG signal classification with a focus on consecutive stages of such a process are reviewed. Examples of EEG classification, considering various types of usage and target applications along with their effectiveness, are also shown.
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- Proceedings of SPA2017 Signal Processing: Algorithms, Architectures, Arrangements, and Application strony 36 - 41
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- MROZIK K. E., Kurowski A., Kostek B., Czyżewski A..: Comparison of selected electroencephalographic signal classification methods, W: Proceedings of SPA2017 Signal Processing: Algorithms, Architectures, Arrangements, and Application, 2017, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.23919/spa.2017.8166834
- Verified by:
- Gdańsk University of Technology
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