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

TL;DR

This paper introduces a versatile, post-hoc labeling framework for EEG data that enables objective evaluation and benchmarking of neural decoding methods without relying on artificial simulations.

Contribution

It proposes a novel, paradigm-agnostic approach for labeling EEG recordings post-hoc, allowing large datasets with noiseless labels for better evaluation of decoding algorithms.

Findings

Enables objective benchmarking of neural decoding methods.

Provides a versatile, realistic alternative to artificial simulation frameworks.

Source code and data are made publicly available.

Abstract

Many cognitive, sensory and motor processes have correlates in oscillatory neural sources, which are embedded as a subspace into the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the use of 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. The latter is specifically problematic, if the artifacts stem from behavioral confounds of the oscillatory neural processes of interest. To overcome some of these problems, simulation frameworks have been introduced for benchmarking decoding methods. Generating artificial brain signals, however, most simulation frameworks make strong and partially unrealistic assumptions about brain activity, which limits the generalization of obtained results to real-world conditions. In the present contribution, we thrive to remove many shortcomings of current simulation frameworks and propose a versatile alternative, that allows for objective evaluation and benchmarking of novel data-driven decoding methods for neural signals. Its central idea is to utilize post-hoc labelings of arbitrary M/EEG recordings. This strategy makes it paradigm-agnostic and allows to generate comparatively large datasets with noiseless labels. Source code and data of the novel simulation approach are made available for facilitating its adoption.