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dc.contributor.author
Hersche, Michael
dc.contributor.author
Benini, Luca
dc.contributor.author
Rahimi, Abbas
dc.date.accessioned
2020-08-17T07:48:25Z
dc.date.available
2020-06-08T05:18:44Z
dc.date.available
2020-08-17T07:48:25Z
dc.date.issued
2020
dc.identifier.isbn
978-1-7281-4922-6
en_US
dc.identifier.isbn
978-1-7281-4923-3
en_US
dc.identifier.other
10.1109/AICAS48895.2020.9073968
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/418756
dc.identifier.doi
10.3929/ethz-b-000387116
dc.description.abstract
Successful motor imagery brain–computer (MI-BCI) algorithms typically rely on a large number of features used in a classifier with real-valued weights that render them unsuitable for real-time execution on a resource-limited device. We propose a new method that randomly projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. Flexibly increasing the dimension of binary embedding achieves almost the same accuracy (≤1.27% lower) compared to all models with float16 in 4-class and 3-class MI, yet delivering a more compact model with simpler operations to execute.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
EEG
en_US
dc.subject
Motor imagery
en_US
dc.subject
Embedding
en_US
dc.subject
Sparse random projection
en_US
dc.subject
Binarized SVM
en_US
dc.subject
Hamming distance
en_US
dc.title
Binary Models for Motor-Imagery Brain–Computer Interfaces: Sparse Random Projection and Binarized SVM
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-04-23
ethz.book.title
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
en_US
ethz.pages.start
163
en_US
ethz.pages.end
167
en_US
ethz.size
5 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020) (virtual)
en_US
ethz.event.location
Genova, Italy
en_US
ethz.event.date
August 31 - September 2, 2020
en_US
ethz.notes
Conference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.
en_US
ethz.grant
Computation-in-memory architecture based on resistive devices
en_US
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.grant.agreementno
780215
ethz.grant.agreementno
780215
ethz.grant.agreementno
780215
ethz.grant.agreementno
780215
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.grant.program
H2020
ethz.grant.program
H2020
ethz.grant.program
H2020
ethz.date.deposited
2019-12-23T19:36:50Z
ethz.source
FORM
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-08-17T07:48:38Z
ethz.rosetta.lastUpdated
2020-08-17T07:48:38Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/387116
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/417484
ethz.COinS
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