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Front Syst Neurosci. 2017 Sep 26;11:61. doi: 10.3389/fnsys.2017.00061. eCollection 2017.

Encoding and Decoding Models in Cognitive Electrophysiology.

Author information

1
Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States.
2
Office of the Vice Chancellor for Research, Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, United States.
3
Department of Psychology, Carl-von-Ossietzky University, Oldenburg, Germany.
4
Institut des Sciences Cognitives Marc Jeannerod, Lyon, France.
5
Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
6
Department of Psychology, University of California, Berkeley, Berkeley, CA, United States.

Abstract

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.

KEYWORDS:

decoding models; electrocorticography (ECoG); electrophysiology/evoked potentials; encoding models; machine learning applied to neuroscience; natural stimuli; predictive modeling; tutorials

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