A convolutional neural network for steady state visual evoked potential classification under ambulatory environmentReport as inadecuate




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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.



Author: No-Sang Kwak, Klaus-Robert Müller, Seong-Whan Lee

Source: http://plos.srce.hr/



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