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Front Hum Neurosci. 2016 Nov 30;10:604. eCollection 2016.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

Author information

1
School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College DublinDublin, Ireland; Department of Pediatrics and Department of Neuroscience, Albert Einstein College of MedicineThe Bronx, NY, USA.
2
School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin Dublin, Ireland.
3
School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College DublinDublin, Ireland; Department of Biomedical Engineering and Department of Neuroscience, University of RochesterRochester, NY, USA.

Abstract

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

KEYWORDS:

EEG/MEG; reverse correlation; sensory processing; stimulus reconstruction; system identification

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