Movement EEG Classification Using Parallel Hidden Markov Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00301765" target="_blank" >RIV/68407700:21230/16:00301765 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/AE.2016.7577243" target="_blank" >http://dx.doi.org/10.1109/AE.2016.7577243</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/AE.2016.7577243" target="_blank" >10.1109/AE.2016.7577243</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Movement EEG Classification Using Parallel Hidden Markov Models

Popis výsledku v původním jazyce

In this contribution we examine the use and utility of parallel HMM classification in single-trial movement-EEG classification of index finger reaching and grasping movement. Parallel HMMs allow us to easily utilize the information contained in multiple channels. Using HMM classifier output in parallel from examined EEG channels we have been able to achieve as good a classification score as with single electrode results, further we do not rely on a single electrode giving persistently good results. Our parallel approach has the added benefit of not having to rely on small inter-session variability as it gives very good results with fewer classifier parameters being optimized. Without any classification optimization we can get a score improvement of 11.2% against randomly selected physiologically relevant electrode. If we use subject specific information we can further improve on the reference score by 1%, achieving a classification score of 84.2+-0.7%.

Název v anglickém jazyce

Movement EEG Classification Using Parallel Hidden Markov Models

Popis výsledku anglicky

In this contribution we examine the use and utility of parallel HMM classification in single-trial movement-EEG classification of index finger reaching and grasping movement. Parallel HMMs allow us to easily utilize the information contained in multiple channels. Using HMM classifier output in parallel from examined EEG channels we have been able to achieve as good a classification score as with single electrode results, further we do not rely on a single electrode giving persistently good results. Our parallel approach has the added benefit of not having to rely on small inter-session variability as it gives very good results with fewer classifier parameters being optimized. Without any classification optimization we can get a score improvement of 11.2% against randomly selected physiologically relevant electrode. If we use subject specific information we can further improve on the reference score by 1%, achieving a classification score of 84.2+-0.7%.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

2016 International Conference on Applied Electronics

ISBN

978-80-261-0601-2

ISSN

1803-7232

e-ISSN

—

Počet stran výsledku

4

Strana od-do

65-68

Název nakladatele

University of West Bohemia

Místo vydání

Pilsen

Místo konání akce

Plzeň

Datum konání akce

6. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391238700014