Comparison of the effectiveness of automatic EEG signal class separation algorithms - Publikacja - MOST Wiedzy

Twoje konto

zaloguj

Wyszukiwarka

Comparison of the effectiveness of automatic EEG signal class separation algorithms

Abstrakt

In this paper, an algorithm for automatic brain activity class identification of EEG (electroencephalographic) signals is presented. EEG signals are gathered from seventeen subjects performing one of the three tasks: resting, watching a music video and playing a simple logic game. The methodology applied consists of several steps, namely: signal acquisition, signal processing utilizing z-score normalization, parametrization and activity classification. The EEG signal is acquired from a headset containing 14 electrodes. For the parametrization two methods are used, namely, DiscreteWavelet Transform (DWT) employed as a reference parametrization technique and autoencoder neural network. Parameters obtained with those methods are fed to the input of classifiers which assigned them to one of three activity classes. Then, the effectiveness of the assignment of the frames of EEG data into appropriate classes is observed and compared. Results obtained using both methods show differences in accuracy with regard to the task detected depending on factors such as type of parametrization or complexity of the classifier employed for EEG activity classification.

Cytowania

  • 0

    CrossRef

  • 0

    Web of Science

  • 0

    Scopus

Autorzy (4)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 24 razy
Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3233/JIFS-179360
Licencja
Copyright (2019 IOS Press)

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS strony 1 - 7,
ISSN: 1064-1246
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Kurowski A., Mrozik K., Kostek B., Czyżewski A.: Comparison of the effectiveness of automatic EEG signal class separation algorithms// JOURNAL OF INTELLIGENT & FUZZY SYSTEMS -, (2019), s.1-7
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3233/jifs-179360
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

wyświetlono 182 razy

Publikacje, które mogą cię zainteresować

Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

2020

Method for Clustering of Brain Activity Data Derived from EEG Signals

2019

Comparison of selected electroencephalographic signal classification methods

2017

Automatic Clustering of EEG-Based Data Associated with Brain Activity

2018
Meta Tagi