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Front. Neuroinform. | doi: 10.3389/fninf.2019.00055

Post-hoc labeling of arbitrary M/EEG recordings for data-efficient evaluation of neural decoding methods

  • 1University of Freiburg, Germany

Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge: the amount of available M/EEG data typically is limited, labels can be unreliable, and raw signals often are contaminated with artifacts. To overcome some of these problems, simulation frameworks have been introduced which support the development of data-driven decoding algorithms and their benchmarking. For generating artificial brain signals, however, most of the existing frameworks make strong and partially unrealistic assumptions about brain activity. This limits the generalization of results observed in the simulation to real-world scenarios.

In the present contribution, we show how to overcome several shortcomings of existing simulation frameworks. We propose a versatile alternative, which allows for an objective evaluation and benchmarking of novel decoding algorithms using real neural signals. It allows to generate comparatively large datasets with labels being deterministically recoverable from the arbitrary M/EEG recordings.
A novel idea to generate these labels is central to this framework: we determine a subspace of the true M/EEG recordings and utilize it to derive novel labels. These labels contain realistic information about the oscillatory activity of some underlying neural sources. For two categories of subspace-defining methods, we showcase how such labels can be obtained---either by an exclusively data-driven approach (independent component analysis---ICA), or by a method exploiting additional anatomical constraints (minimum norm estimates---MNE). We term our framework \textit{post-hoc labeling} of M/EEG recordings.
To support the adoption of the framework by practitioners, we have exemplified its use by benchmarking three standard decoding methods---i.e., common spatial patterns (CSP), source power-comodulation (SPoC), and convolutional neural networks (ConvNets)---wrt. Varied dataset sizes, label noise, and label variability. Source code and data are made available to the reader for facilitating the application of our post-hoc labeling framework.

Keywords: neural decoding, M/EEG labeling, data-driven neural decoding, Brain Computer Interfaces (BCIs), band-power decoding

Received: 28 Feb 2019; Accepted: 08 Jul 2019.

Edited by:

Jose M. Ferrandez, Universidad Politécnica de Cartagena, Spain

Reviewed by:

Vinay Jayaram, Max Planck Institute for Intelligent Systems, Germany
Manuel G. Nomay, University of the Basque Country, Spain  

Copyright: © 2019 Castaño-Candamil, Meinel and Tangermann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mr. Sebastian Castaño-Candamil, University of Freiburg, Freiburg, Germany, sebastian.castano@blbt.uni-freiburg.de
Dr. Michael Tangermann, University of Freiburg, Freiburg, Germany, michael.tangermann@blbt.uni-freiburg.de