ArticleFrontiers in human neuroscience2016
The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 297 papers.
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297 citing papers in PubMed.
- Longitudinal adaptations in neural and behavioral systems following hearing restoration using cochlear implants.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- "When the word ends, the story begins": word offsets improve encoding of natural speech in a MEG study.Brain topography · 2026Article
- Simulated field potentials capture multiple simultaneous processes underlying human temporal attention.bioRxiv : the preprint server for biology · 2026Article
- Thalamocortical network dynamics can explain visual impulse responses and perceptual echoes.iScience · 2026Article
- Article
- [Dual-stream temporal-frequency convolutional network with additive attention for auditory attention decoding].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Acoustic Scene-Aware Processing and Auditory Model-Based Compensation Strategies.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Infant Cortical Tracking of Speech Shows Emerging Spatial Release from Masking in the First Year of Life.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Bouncing back: Transient, but not long-lasting, effects of background disturbances on speech processing.bioRxiv : the preprint server for biology · 2026Article
- Articulatory timing and form support distinct neural benefits during audiovisual speech.bioRxiv : the preprint server for biology · 2026Article
- Salient features of task-irrelevant continuous speech distort subjective time.Journal of experimental psychology. General · 2026Article
- Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability.The European journal of neuroscience · 2026Article
- The Effect of Task Demand on EEG Responses to Irrelevant Sound and Speech in Simulated Surgical Environments.Psychophysiology · 2026Article
- Frontal cortex organization supporting audiovisual processing during naturalistic viewing.Nature communications · 2026Article
- Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?The European journal of neuroscience · 2026Review
- Cognitive load weakens neural speech tracking without altering response timing.Communications biology · 2026Article
- Decoding selective auditory attention to musical elements in ecologically valid music listening.Scientific reports · 2026Article
- The shape of attention reflects flexible filtering of natural speech modulations.Communications biology · 2026Article
- During natural vision, semantic novelty modulates fixation-related processing in primate cortex.bioRxiv : the preprint server for biology · 2026Article
- Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG.The European journal of neuroscience · 2026Article
237 more citing papers are in PubMed but not listed here.
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4 authors.
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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.
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