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dc.contributor.author
Hersche, Michael
dc.contributor.author
Benini, Luca
dc.contributor.author
Rahimi, Abbas
dc.date.accessioned
2020-12-22T08:02:24Z
dc.date.available
2020-12-22T07:43:48Z
dc.date.available
2020-12-22T08:02:24Z
dc.date.issued
2020-12
dc.identifier.issn
2156-3357
dc.identifier.other
10.1109/jetcas.2020.3031698
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/457995
dc.identifier.doi
10.3929/ethz-b-000457995
dc.description.abstract
Successful motor-imagery brain–computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolutional neural networks (CNNs). Both approaches typically result in a set of real-valued weights, that pose challenges when targeting real-time execution on tightly resource-constrained devices. We propose methods for each of these approaches that allow transforming real-valued weights to binary numbers for efficient inference. Our first method, based on sparse bipolar random projection, 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. By tuning the dimension of the binary embedding, we achieve almost the same accuracy in 4-class MI (≤1.27% lower) compared to models with float16 weights, yet delivering a more compact model with simpler operations to execute. Second, we propose to use memory-augmented neural networks (MANNs) for MI-BCI such that the augmented memory is binarized. Our method replaces the fully connected layer of CNNs with a binary augmented memory using bipolar random projection, or learned projection. Our experimental results on EEGNet, an already compact CNN for MI-BCI, show that it can be compressed by 1.28x at iso-accuracy using the random projection. On the other hand, using the learned projection provides 3.89% higher accuracy but increases the memory size by 28.10x.
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
Convolutional Neural Networks
en_US
dc.subject
Sparse random projection
en_US
dc.subject
Support Vector Machine
en_US
dc.subject
memory-augmented neural networks
en_US
dc.title
Binarization Methods for Motor-Imagery Brain–Computer Interface Classification
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-10-16
ethz.journal.title
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
ethz.journal.volume
10
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
IEEE j. emerg. sel. top. circuits syst.
ethz.pages.start
567
en_US
ethz.pages.end
577
en_US
ethz.size
12 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.grant
Human Augmentation Interfaces
en_US
ethz.grant
Computation-in-memory architecture based on resistive devices
en_US
ethz.identifier.wos
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
ETH-09 18-2
ethz.grant.agreementno
780215
ethz.grant.fundername
ETHZ
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100003006
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
ETH Grants
ethz.grant.program
H2020
ethz.date.deposited
2020-12-22T07:43:55Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-12-22T08:02:32Z
ethz.rosetta.lastUpdated
2022-03-29T04:38:52Z
ethz.rosetta.versionExported
true
ethz.COinS
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