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

2016 ◽  
Vol 10 ◽  
Author(s):  
Michael J. Crosse ◽  
Giovanni M. Di Liberto ◽  
Adam Bednar ◽  
Edmund C. Lalor
Keyword(s):  
Response Function ◽  
Temporal Response ◽  
Neural Signals ◽  
Matlab Toolbox

scholarly journals Comprehension under noise in L1 and L2: Acoustic entrainment of speech envelope

2021 ◽  
Author(s):  
Benjamin D. Zinszer ◽  
Qiming Yuan ◽  
Zhaoqi Zhang ◽  
Bharath Chandrasekaran ◽  
Taomei GUO
Keyword(s):  
Response Function ◽  
Temporal Response ◽  
L2 Proficiency ◽  
Continuous Speech ◽  
Noisy Conditions ◽  
Speech Envelope ◽  
Cortical Entrainment ◽  
L1 And L2

Listeners regularly comprehend continuous speech despite noisy conditions. Previous studies show that cortical entrainment to speech degrades under noise, predicts comprehension, and increases for non-native listeners. We test the hypothesis that listeners similarly increase cortical entrainment for both L2 and noisy L1 speech, after controlling for comprehension. Twenty-four Chinese-English bilinguals underwent EEG while listening to one hour of an audiobook, mixed with three levels of noise, in Mandarin and English and answered comprehension questions. We estimated cortical entrainment for one-minute tracks using the multivariate temporal response function (mTRF). Contrary to our prediction, entrainment of the L2 was significantly lower than L1, while L1 entrainment significantly increased when speech was masked by noise without reducing comprehension. However, greater L2 proficiency was positively associated with greater entrainment. We discuss how studies of entrainment relating to noise and bilingualism might be reconciled with an approach focused on exerted rather than demanded effort.

scholarly journals Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

2019 ◽  
Author(s):  
Neetha Das ◽  
Jonas Vanthornhout ◽  
Tom Francart ◽  
Alexander Bertrand
Keyword(s):  
Response Function ◽  
Signal To Noise Ratio ◽  
Spatial Filtering ◽  
Neural Response ◽  
Eigenvalue Decomposition ◽  
Neural Responses ◽  
Temporal Response ◽  
Neural Data ◽  
Brain Functions ◽  
Eeg Data

AbstractObjective. Neural responses recorded using electroencephalography (EEG) and magnetoencephalography (MEG) can be used to study how our brain functions, as well as for various promising brain computer interface (BCI) applications. However, a common problem is the low signal to noise ratio (SNR) which makes it challenging to estimate task-related neural responses or the temporal response function (TRF) describing the linear relationship between the stimulus and the neural response, particularly over short data windows. To address these, we present an algorithm that takes advantage of the multi-channel nature of the recordings, and knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction using spatial filtering. Methods. Forward modeling is used to project the stimulus onto the electrode space. The second-order statistics of this estimated desired signal and the raw neural data are used to estimate spatial filters that maximize the SNR of the neural response, based on a generalized eigenvalue decomposition. Main Results. 1. For synthesized EEG data, over a range of SNRs, our filtering resulted in significantly better TRF estimates from 20 s trials, compared to unfiltered EEG data. 2. On a dataset from 28 subjects who listened to a single-talker stimulus, our method resulted in correlations between predicted neural responses and the original EEG data that were significantly higher compared to standard approaches. 3. On a dataset of 16 subjects attending to 1 speaker in a two-speaker scenario, our method resulted in attention decoding accuracies which were higher compared to existing forward modelling methods. Significance. Our algorithm presents a data-driven way to denoise and reduce dimensionality of neural data, thus aiding further analysis, by utilizing the knowledge of the stimulus. The method is computationally efficient, and does not require repeated trials, thereby relieving experiment design from the necessity of presenting repeated stimuli to the subjects.

Determination of the temporal response function in femtosecond pump-probe systems

2001 ◽  
Vol 72 (7) ◽  
pp. 843-847 ◽  
Author(s):  
M. Ziólek ◽  
M. Lorenc ◽  
R. Naskrecki
Keyword(s):  
Response Function ◽  
Temporal Response ◽  
Pump Probe

Visual Cortex Neurons of Monkeys and Cats: Temporal Dynamics of the Contrast Response Function

2002 ◽  
Vol 88 (2) ◽  
pp. 888-913 ◽  
Author(s):  
Duane G. Albrecht ◽  
Wilson S. Geisler ◽  
Robert A. Frazor ◽  
Alison M. Crane
Keyword(s):  
Visual Cortex ◽  
Response Function ◽  
Temporal Dynamics ◽  
Gain Control ◽  
Temporal Response ◽  
Contrast Response Function ◽  
Visual Cortex Neurons ◽  
Contrast Normalization ◽  
Single Fixation ◽  
Contrast Response

Cortical neurons display two fundamental nonlinear response characteristics: contrast-set gain control (also termed contrast normalization) and response expansion (also termed half-squaring). These nonlinearities could play an important role in forming and maintaining stimulus selectivity during natural viewing, but only if they operate well within the time frame of a single fixation. To analyze the temporal dynamics of these nonlinearities, we measured the responses of individual neurons, recorded from the primary visual cortex of monkeys and cats, as a function of the contrast of transient stationary gratings that were presented for a brief interval (200 ms). We then examined 1) the temporal response profile (i.e., the post stimulus time histogram) as a function of contrast and 2) the contrast response function throughout the course of the temporal response. We found that the shape and complexity of the temporal response profile varies considerably from cell to cell. However, within a given cell, the shape remains relatively invariant as a function of contrast and appears to be simply scaled and shifted. Stated quantitatively, approximately 95% of the variation in the temporal responses as a function of contrast could be accounted for by scaling and shifting the average poststimulus time histogram. Equivalently, we found that the overall shape of the contrast response function (measured every 2 ms) remains relatively invariant from the onset through the entire temporal response. Further, the contrast-set gain control and the response expansion are fully expressed within the first 10 ms after the onset of the response. Stated quantitatively, the same, scaled Naka-Rushton equation (with the same half-saturation contrast and expansive response exponent) provides a good fit to the contrast response function from the first 10 ms through the last 10 ms of the temporal response. Based upon these measurements, it appears as though the two nonlinear properties, contrast-set gain control and response expansion, are present in full strength, virtually instantaneously, at the onset of the response. This observation suggests that response expansion and contrast-set gain control can influence the performance of visual cortex neurons very early in a single fixation, based on the contrast within that fixation. In the discussion, we consider the implications of the results within the context of 1) slower types of contrast gain control, 2) discrimination performance, 3) drifting steady-state measurements, 4) functional models that incorporate response expansion and contrast normalization, and 5) structural models of the biochemical and biophysical neural mechanisms.

scholarly journals Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses

2019 ◽  
Vol 62 (3) ◽  
pp. 587-601 ◽  
Author(s):  
Zilong Xie ◽  
Rachel Reetzke ◽  
Bharath Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Response Function ◽  
Language Processing ◽  
Speech Stimulus ◽  
Temporal Response ◽  
Speech And Language ◽  
Continuous Speech ◽  
Classification Approach ◽  
Phonological Categories ◽  
Speech And Language Processing

Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)–based approaches to analyzing speech-evoked neurophysiological responses. Method Two categories of ML-based approaches are introduced: decoding models, which generate a speech stimulus output using the features from the neurophysiological responses, and encoding models, which use speech stimulus features to predict neurophysiological responses. In this review, we focus on (a) a decoding model classification approach, wherein speech-evoked neurophysiological responses are classified as belonging to 1 of a finite set of possible speech events (e.g., phonological categories), and (b) an encoding model temporal response function approach, which quantifies the transformation of a speech stimulus feature to continuous neural activity. Results We illustrate the utility of the classification approach to analyze early electroencephalographic (EEG) responses to Mandarin lexical tone categories from a traditional experimental design, and to classify EEG responses to English phonemes evoked by natural continuous speech (i.e., an audiobook) into phonological categories (plosive, fricative, nasal, and vowel). We also demonstrate the utility of temporal response function to predict EEG responses to natural continuous speech from acoustic features. Neural metrics from the 3 examples all exhibit statistically significant effects at the individual level. Conclusion We propose that ML-based approaches can complement traditional analysis approaches to analyze neurophysiological responses to speech signals and provide a deeper understanding of natural speech and language processing using ecologically valid paradigms in both typical and clinical populations.

scholarly journals Accurate Decoding of Imagined and Heard Melodies

2021 ◽  
Vol 15 ◽  
Author(s):  
Giovanni M. Di Liberto ◽  
Guilhem Marion ◽  
Shihab A. Shamma
Keyword(s):  
Music Perception ◽  
Low Frequency ◽  
Response Functions ◽  
Temporal Response ◽  
Neural Signals ◽  
Professional Musicians ◽  
Related Information ◽  
Single Subjects ◽  
Potential Applications ◽  
Envelope Reconstruction

Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRFenv). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.

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