Space-by-time decomposition for single-trial decoding of M/EEG activity

TY - JOUR

T1 - Space-by-time decomposition for single-trial decoding of M/EEG activity

AU - Delis, Ioannis

AU - Onken, Arno

AU - Schyns, Philippe G

AU - Panzeri, Stefano

AU - Philiastides, Marios G

N1 - Copyright © 2016 Elsevier Inc. All rights reserved.

PY - 2016/6

Y1 - 2016/6

N2 - 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.

AB - 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.

KW - Algorithms

KW - Brain Mapping/methods

KW - Electroencephalography/methods

KW - Female

KW - Humans

KW - Image Interpretation, Computer-Assisted/methods

KW - Magnetoencephalography/methods

KW - Male

KW - Pattern Recognition, Visual/physiology

KW - Reproducibility of Results

KW - Sensitivity and Specificity

KW - Spatio-Temporal Analysis

KW - Visual Cortex/physiology

KW - Young Adult

U2 - 10.1016/j.neuroimage.2016.03.043

DO - 10.1016/j.neuroimage.2016.03.043

M3 - SCORING: Journal article

C2 - 27033682

VL - 133

SP - 504

EP - 515

JO - NEUROIMAGE

JF - NEUROIMAGE

SN - 1053-8119

ER -