Please use this identifier to cite or link to this item:
For citation please use:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKwak, No-Sang-
dc.contributor.authorMüller, Klaus-Robert-
dc.contributor.authorLee, Seong-Whan-
dc.description.abstractThe robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN’s robust, accurate decoding abilities.en
dc.subject.ddc610 Medizin und Gesundheiten
dc.subject.ddc004 Datenverarbeitung; Informatiken
dc.subject.otherconvolutional neural networken
dc.subject.otherbrain machine interfaceen
dc.titleA convolutional neural network for steady state visual evoked potential classification under ambulatory environmenten
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dcterms.bibliographicCitation.journaltitlePLOS ONEen
dcterms.bibliographicCitation.originalpublisherplaceSan Francisco, Calif.en
dcterms.bibliographicCitation.originalpublishernamePublic Library of Science (PLOS)en
tub.affiliationFak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Maschinelles Lernende
Appears in Collections:Technische Universität Berlin » Publications

Files in This Item:
Format: Adobe PDF | Size: 2.82 MB
DownloadShow Preview

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons