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Main Title: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment
Author(s): Kwak, No-Sang
Müller, Klaus-Robert
Lee, Seong-Whan
Type: Article
Language Code: en
Abstract: The 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.
Issue Date: 22-Feb-2017
Date Available: 26-Nov-2020
DDC Class: 610 Medizin und Gesundheit
004 Datenverarbeitung; Informatik
Subject(s): convolutional neural network
brain machine interface
Journal Title: PLOS ONE
Publisher: Public Library of Science (PLOS)
Publisher Place: San Francisco, Calif.
Volume: 12
Issue: 2
Article Number: e0172578
Publisher DOI: 10.1371/journal.pone.0172578
EISSN: 1932-6203
Appears in Collections:FG Maschinelles Lernen » Publications

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