Delis, I, Onken, A, Schyns, G et al. (2 more authors) (2016) Space-by-time decomposition for single-trial decoding of M/EEG activity. NeuroImage, 133. pp. 504-515. ISSN 1095-9572
Abstract
We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illustrate the effectiveness of the proposed approach on an EEG dataset recorded during the performance of a visual categorization task. Our method extracts three temporal and two spatial functional components achieving a compact yet full representation of the underlying structure, which validates and summarizes succinctly results from previous studies. Furthermore, we introduce a decoding analysis that allows determining the distinct functional role of each component and relating them to experimental conditions and task parameters. In particular, we demonstrate that the presented stimulus and the task difficulty of each trial can be reliably decoded using specific combinations of components from the identified space-by-time representation. When comparing with a sliding-window linear discriminant algorithm, we show that our approach yields more robust decoding performance across participants. Overall, our findings suggest that the proposed space-by-time decomposition is a meaningful low-dimensional representation that carries the relevant information of single-trial M/EEG signals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | M/EEG; NMF; Single-trial analysis; Neural representation; Dimensionality reduction; Decoding |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 May 2018 15:27 |
Last Modified: | 09 May 2018 15:27 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.neuroimage.2016.03.043 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127520 |