While there are several classes of models that can be implemented for this purpose (reviewed in Wu et al., 2006), the most straightforward class are linear time-invariant (LTI) systems. While this approach has revealed novel and important insights into the neurophysiology of speech processing, it does not facilitate characterization of the system's response function, and in any case, is an indirect measure of how the brain entrains to the stimulus over time.Ī more direct way to investigate neural entrainment to continuous stimuli is to mathematically model a function that describes the way a particular property of the stimulus is mapped onto neural responses, a technique known as system identification (SI Marmarelis, 2004). Recent studies have begun to use more naturalistic, extended speech stimuli by focusing their analysis on measuring the phase of neural responses across multiple repetitions of the same speech segment ( Luo and Poeppel, 2007 Zion-Golumbic et al., 2013). However, the type of speech stimuli used in such ERP studies usually consist of individual phonemes or syllables and are therefore not entirely reflective of natural, connected speech which is ongoing and abundant with lexical complexity.
This approach has been used extensively to study how the human brain processes various ecological events, even those that occur in a continuous, dynamic manner such as human speech (e.g., Salmelin, 2007 Picton, 2013). The objective is to estimate the impulse response function of the sensory system under investigation by convolving the system with a transient, impulse-like stimulus and averaging over several-hundred time-locked response trials. Traditionally, research on the electrophysiology of sensory processing in humans has focused on the rather special case of brief, isolated stimuli because of the need to time-lock to discrete sensory events in order to estimate event-related potentials (ERPs Handy, 2005 Luck, 2014). Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We then introduce a new open-source toolbox for performing this analysis.
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. 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.
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. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. 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. Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. 3Department of Biomedical Engineering and Department of Neuroscience, University of Rochester, Rochester, NY, USA.2Department of Pediatrics and Department of Neuroscience, Albert Einstein College of Medicine, The Bronx, NY, USA.1School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.Di Liberto 1, Adam Bednar 1,3 and Edmund C.
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