Biology subjects

de Cheveigne, A.

Publications and source records attributed to de Cheveigne, A..

6 recordsLinked to original sources

bioRxiv2018

Cortical responses to natural speech reflect probabilistic phonotactics

Humans comprehend speech despite the various challenges of real-world environments, such as loud noise and mispronunciation. Our auditory system is robust to these thanks to the integration of the upcoming sensory input with prior knowledge and expectations built on language-specific regularities. One such regularity regards the permissible phoneme sequences, which determine the likelihood that a word belongs to a given language (phonotactic probability; \"blick\" is more likely to be an English word than \"bnick\"). Previous research suggested that violations of these rules modulate brain evoked responses such as the N400 and the late positive complex. Yet several fundamental questions remain unresolved, especially regarding the neural encoding and integration strategy of phonotactic information. Here, we used linear modelling approaches to assess the influence of phonotactic probabilities on the brain responses to narrative speech measured with non-invasive EEG. We found that the relationship between continuous speech and EEG responses is best described when the speech descriptor includes phonotactic probabilities. This provides us with a methodology to isolate and measure the brain responses to phonotactics using natural speech at the individual subject-level. Furthermore, such low-frequency signals showed the strongest speech-EEG interactions at latencies of 100-400 ms, supporting a pre-lexical role of phonotactic information.\n\nSignificance StatementSpeech is composed of basic units, called phonemes, whose combinations comply with language-specific regularities determining whether a sequence \"sounds\" as a plausible word. Our ability to detect irregular combinations requires matching incoming sequences with our internal expectations, a process that supports speech segmentation and learning. However, the neural mechanisms underlying this phenomenon have not yet been established. Here, we examine this in the human brain using narrative speech. We identified a brain signal reflecting the likelihood that a word belongs to the language, which may offer new opportunities to investigate speech perception, learning, development, and impairment. Our data also suggest a pre-lexical role of this phenomenon, thus supporting and extending current mechanistic perspectives.

neuroscience↗

bioRxiv2018

Multiway Canonical Correlation Analysis of Brain Signals

Brain signals recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratio due to the presence of multiple competing sources and artifacts. A common remedy is to average over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with the same identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

neuroscience↗

bioRxiv2018

A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.

neuroscience↗

bioRxiv2017

Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are are mainly automatic and require little tuning, can greatly improve the quality of the data.

neuroscience↗

bioRxiv2017

Decoding the auditory brain with canonical component analysis

The relation between a stimulus and the brain response that it evokes can shed light on perceptual processes within the brain. It can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated \"decoding\" strategies are needed to address ongoing stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both stimulus and response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves our observation of the relation between stimulus and response.

neuroscience↗

bioRxiv2017

Changes In Firing Regularity In The Normal And Impaired Auditory System

The auditory system processes temporal information at multiple scales, and disruptions to this temporal processing may lead to deficits in auditory tasks such as detecting and discriminating sounds in a noisy environment. Here, a modelling approach is used to study the temporal regularity of firing by chopper cells in the ventral cochlear nucleus, in both the normal and impaired auditory system. Chopper cells, which have a strikingly regular firing response, divide into two classes, sustained and transient, based on the time course of this regularity. Several hypotheses have been proposed to explain the behaviour of chopper cells, and the difference between sustained and transient cells in particular. However, there is no conclusive evidence so far. Here, a reduced mathematical model is developed and used to compare and test a wide range of hypotheses with a limited number of parameters. Simulation results show a continuum of cell types and behaviours: chopper-like behaviour arises for a wide range of parameters, suggesting that multiple mechanisms may underlie this behaviour. The model accounts for systematic trends in regularity as a function of stimulus level that have previously only been reported anecdotally. Finally, the model is used to predict the effects of a reduction in the number of auditory nerve fibres (deafferentation due to, for example, cochlear synaptopathy). An interactive version of this paper in which all the model parameters can be changed is available online.\n\nHighlightsO_LIA low parameter model reproduces chopper cell firing regularity\nC_LIO_LIMultiple factors can account for sustained vs transient chopper cell response\nC_LIO_LIThe model explains stimulus level dependence of firing regularity\nC_LIO_LIThe model predicts chopper cells fire more irregularly after deafferentation\nC_LIO_LIAn interactive version of the paper allows readers to change parameters\nC_LI

neuroscience↗