Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Brain-computer interfaces (BCIs) might provide
an intuitive way for severely motor impaired persons to operate
assistive devices to perform daily life activities. Recent studies
have shown that complex hand movements, such as reachand-
grasp tasks, can be decoded from the low frequency of
the electroencephalogram (EEG). In this work we investigated
whether additional features extracted from the frequencydomain
of alpha and beta bands could improve classification
performance of rest vs. palmar vs. lateral grasp. We analysed
two multi-class classification approaches, the first using features
from the low frequency time-domain, and the second in which
we combined the time-domain with frequency-domain features
from alpha and beta bands. We measured EEG of ten participants
without motor disability which performed self-paced
reach-and-grasp actions on objects of daily life. For the timedomain
classification approach, participants reached an average
peak accuracy of 65%. For the combined approach, an average
peak accuracy of 75% was reached. In both approaches and for
all subjects, performance was significantly higher than chance
level (38.1%, 3-class scenario). By computing the confusion
matrices as well as feature rankings through the Fisher score,
we show that movement vs. rest classification performance
increased considerably in the combined approach and was
the main responsible for the multi-class higher performance.
These findings could help the development of BCIs in real-life
scenarios, where decreasing false movement detections could
drastically increase the end-user acceptance and usability of
BCIs.
Originalspracheenglisch
TitelProceedings of the 41st International Engineering in Medicine and Biology Conference
PublikationsstatusAngenommen/In Druck - 25 Jul 2019
Veranstaltung41st International Engineering in Medicine and Biology Conference - CityCube, Berlin, Deutschland
Dauer: 23 Jul 201927 Jul 2019
https://embc.embs.org/2019/

Konferenz

Konferenz41st International Engineering in Medicine and Biology Conference
KurztitelEMBC2019
LandDeutschland
OrtBerlin
Zeitraum23/07/1927/07/19
Internetadresse

Fingerprint

Brain computer interface
Hand Strength
Electroencephalography
Brain-Computer Interfaces
Hand
Equipment and Supplies

ASJC Scopus subject areas

  • !!Signal Processing
  • Human-computer interaction
  • !!Neuroscience (miscellaneous)

Fields of Expertise

  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Dies zitieren

Schwarz, A., Pereira, J., Lindner, L., & Müller-Putz, G. (Angenommen/Im Druck). Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions. in Proceedings of the 41st International Engineering in Medicine and Biology Conference

Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions. / Schwarz, Andreas; Pereira, Joana; Lindner, Lydia; Müller-Putz, Gernot.

Proceedings of the 41st International Engineering in Medicine and Biology Conference. 2019.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Schwarz, A, Pereira, J, Lindner, L & Müller-Putz, G 2019, Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions. in Proceedings of the 41st International Engineering in Medicine and Biology Conference., Berlin, Deutschland, 23/07/19.
Schwarz A, Pereira J, Lindner L, Müller-Putz G. Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions. in Proceedings of the 41st International Engineering in Medicine and Biology Conference. 2019
Schwarz, Andreas ; Pereira, Joana ; Lindner, Lydia ; Müller-Putz, Gernot. / Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions. Proceedings of the 41st International Engineering in Medicine and Biology Conference. 2019.
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title = "Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions",
abstract = "Brain-computer interfaces (BCIs) might providean intuitive way for severely motor impaired persons to operateassistive devices to perform daily life activities. Recent studieshave shown that complex hand movements, such as reachand-grasp tasks, can be decoded from the low frequency ofthe electroencephalogram (EEG). In this work we investigatedwhether additional features extracted from the frequencydomainof alpha and beta bands could improve classificationperformance of rest vs. palmar vs. lateral grasp. We analysedtwo multi-class classification approaches, the first using featuresfrom the low frequency time-domain, and the second in whichwe combined the time-domain with frequency-domain featuresfrom alpha and beta bands. We measured EEG of ten participantswithout motor disability which performed self-pacedreach-and-grasp actions on objects of daily life. For the timedomainclassification approach, participants reached an averagepeak accuracy of 65{\%}. For the combined approach, an averagepeak accuracy of 75{\%} was reached. In both approaches and forall subjects, performance was significantly higher than chancelevel (38.1{\%}, 3-class scenario). By computing the confusionmatrices as well as feature rankings through the Fisher score,we show that movement vs. rest classification performanceincreased considerably in the combined approach and wasthe main responsible for the multi-class higher performance.These findings could help the development of BCIs in real-lifescenarios, where decreasing false movement detections coulddrastically increase the end-user acceptance and usability ofBCIs.",
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AB - Brain-computer interfaces (BCIs) might providean intuitive way for severely motor impaired persons to operateassistive devices to perform daily life activities. Recent studieshave shown that complex hand movements, such as reachand-grasp tasks, can be decoded from the low frequency ofthe electroencephalogram (EEG). In this work we investigatedwhether additional features extracted from the frequencydomainof alpha and beta bands could improve classificationperformance of rest vs. palmar vs. lateral grasp. We analysedtwo multi-class classification approaches, the first using featuresfrom the low frequency time-domain, and the second in whichwe combined the time-domain with frequency-domain featuresfrom alpha and beta bands. We measured EEG of ten participantswithout motor disability which performed self-pacedreach-and-grasp actions on objects of daily life. For the timedomainclassification approach, participants reached an averagepeak accuracy of 65%. For the combined approach, an averagepeak accuracy of 75% was reached. In both approaches and forall subjects, performance was significantly higher than chancelevel (38.1%, 3-class scenario). By computing the confusionmatrices as well as feature rankings through the Fisher score,we show that movement vs. rest classification performanceincreased considerably in the combined approach and wasthe main responsible for the multi-class higher performance.These findings could help the development of BCIs in real-lifescenarios, where decreasing false movement detections coulddrastically increase the end-user acceptance and usability ofBCIs.

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