Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion

Piotr Szczuko

doi:10.1109/spa.2016.7763583

Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion

Abstract

A rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion. Results are commented and further research is proposed.

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Piotr Szczuko dr hab. inż.

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Category:: Conference activity
Type:: materiały konferencyjne indeksowane w Web of Science
Title of issue:: 20th IEEE Conference on Signal Processing - Algorithms, Architectures, Arrangements, and Applications (SPA) strony 34 - 39
Language:: English
Publication year:: 2016
Bibliographic description:: Szczuko P..: Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion, W: 20th IEEE Conference on Signal Processing - Algorithms, Architectures, Arrangements, and Applications (SPA), 2016, Politechnika Poznańska,.
DOI:: Digital Object Identifier (open in new tab) 10.1109/spa.2016.7763583
Verified by:: Gdańsk University of Technology