Interpreting neural decoding models using grouped model reliance.

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ID: 78983
2020
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Abstract
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, there is a need for methods that allow to interpret trained decoding models. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0-9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. The present results confirm previous findings insofar as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and topography varied considerably between individuals, pointing to more pronounced inter-individual differences than previously reported.
Reference Key
valentin2020interpretingplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Valentin, Simon;Harkotte, Maximilian;Popov, Tzvetan;
Journal PLoS computational biology
Year 2020
DOI
10.1371/journal.pcbi.1007148
URL
Keywords
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