EEG Feature Selection Based on Time Series Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212220" target="_blank" >RIV/68407700:21230/13:00212220 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12410/13:43885709

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-39712-7_40" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-39712-7_40</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-39712-7_40" target="_blank" >10.1007/978-3-642-39712-7_40</a>

Jazyk výsledku

angličtina

Název v původním jazyce

EEG Feature Selection Based on Time Series Classification

Popis výsledku v původním jazyce

We propose novel method of EEG signal analysis based on classification of feature time series. The algorithm classifies sequences of feature values and it calculates the error rate both for each time step and overall sequence. We compared the performanceof the algorithm with a standard feature selection method based on forward inter-intra criterion. Both algorithms selected similar features. The algorithm was tested on the EEG data from 2 experiments focused on of spatial navigation and orientation. Participants traversed through the virtual tunnels and they could adopt two different reference frames (allocenctric and egocentric) to solve the task. The EEG signal was recorded within both tasks and the methods of feature extraction and both standard and timeseries selection and classification were applied to it. We identified differences between the groups of participants adopting allocentric and egocentric frames of reference in the parietal and central electrodes in right hemisphere.

Název v anglickém jazyce

EEG Feature Selection Based on Time Series Classification

Popis výsledku anglicky

We propose novel method of EEG signal analysis based on classification of feature time series. The algorithm classifies sequences of feature values and it calculates the error rate both for each time step and overall sequence. We compared the performanceof the algorithm with a standard feature selection method based on forward inter-intra criterion. Both algorithms selected similar features. The algorithm was tested on the EEG data from 2 experiments focused on of spatial navigation and orientation. Participants traversed through the virtual tunnels and they could adopt two different reference frames (allocenctric and egocentric) to solve the task. The EEG signal was recorded within both tasks and the methods of feature extraction and both standard and timeseries selection and classification were applied to it. We identified differences between the groups of participants adopting allocentric and egocentric frames of reference in the parietal and central electrodes in right hemisphere.

Druh

D - Stať ve sborníku

CEP obor

AN - Psychologie

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-642-39711-0

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

8

Strana od-do

520-527

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

New York

Datum konání akce

19. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—