PERMUTOOLS: A MATLAB PACKAGE FOR MULTIVARIATE PERMUTATION TESTING.

Statistical hypothesis testing and effect size measurement are routine parts of quantitative research. Advancements in computer processing power have greatly improved the capability of statistical inference through the availability of resampling methods. However, many of the statistical practices used today are based on traditional, parametric methods that rely on assumptions about the underlying population. These assumptions may not always be valid, leading to inaccurate results and misleading interpretations. Permutation testing, on the other hand, generates the sampling distribution empirically by permuting the observed data, providing distribution-free hypothesis testing. Furthermore, this approach lends itself to a powerful method for multiple comparison correction - known as max correction - which is less prone to type II errors than conventional correction methods. Parametric methods have also traditionally been utilized for estimating the confidence interval of various test statistics and effect size measures. However, these too can be estimated empirically using permutation or bootstrapping techniques. Whilst resampling methods are generally considered preferable, many popular programming languages and statistical software packages lack efficient implementations. Here, we introduce PERMUTOOLS, a MATLAB package for multivariate permutation testing and effect size measurement.

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli.

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. The framework has been designed to interface easily with existing toolboxes, such as EelBrain, NapLib, MNE, and the mTRF-Toolbox. We present guidelines by taking both the user view (how to rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share), making the process as straightforward and accessible as possible for all users. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.

Seeing a Talking Face Matters: Gaze Behavior and the Auditory-Visual Speech Benefit in Adults' Cortical Tracking of Infant-directed Speech.

In face-to-face conversations, listeners gather visual speech information from a speaker's talking face that enhances their perception of the incoming auditory speech signal. This auditory-visual (AV) speech benefit is evident even in quiet environments but is stronger in situations that require greater listening effort such as when the speech signal itself deviates from listeners' expectations. One example is infant-directed speech (IDS) presented to adults. IDS has exaggerated acoustic properties that are easily discriminable from adult-directed speech (ADS). Although IDS is a speech register that adults typically use with infants, no previous neurophysiological study has directly examined whether adult listeners process IDS differently from ADS. To address this, the current study simultaneously recorded EEG and eye-tracking data from adult participants as they were presented with auditory-only (AO), visual-only, and AV recordings of IDS and ADS. Eye-tracking data were recorded because looking behavior to the speaker's eyes and mouth modulates the extent of AV speech benefit experienced. Analyses of cortical tracking accuracy revealed that cortical tracking of the speech envelope was significant in AO and AV modalities for IDS and ADS. However, the AV speech benefit [i.e., AV > (A + V)] was only present for IDS trials. Gaze behavior analyses indicated differences in looking behavior during IDS and ADS trials. Surprisingly, looking behavior to the speaker's eyes and mouth was not correlated with cortical tracking accuracy. Additional exploratory analyses indicated that attention to the whole display was negatively correlated with cortical tracking accuracy of AO and visual-only trials in IDS. Our results underscore the nuances involved in the relationship between neurophysiological AV speech benefit and looking behavior.

Neurophysiological measures of auditory sensory processing are associated with adaptive behavior in children with Autism Spectrum Disorder.

BACKGROUND: Atypical auditory cortical processing is consistently found in scalp electrophysiological and magnetoencephalographic studies of Autism Spectrum Disorder (ASD), and may provide a marker of neuropathological brain development. However, the relationship between atypical cortical processing of auditory information and adaptive behavior in ASD is not yet well understood. METHODS: We sought to test the hypothesis that early (100-175 ms) auditory processing in ASD is related to everyday adaptive behavior through the examination of auditory event-related potentials (AEPs) in response to simple tones and Vineland Adaptive Behavior Scales in a large cohort of children with ASD (N = 84), aged 6-17, and in age- and IQ- matched neurotypically (NT) developing controls (N = 132). RESULTS: Statistical analyses revealed significant group differences in early AEPs over temporal scalp regions (150-175 ms), and the expected rightward lateralization of the AEP (100-125 ms and 150-175 ms) to tonal stimuli in both groups. Lateralization of the AEP (150-175 ms) was significantly associated with adaptive functioning in the socialization domain. CONCLUSIONS: These results lend support to the hypothesis that atypical processing of sensory information is related to everyday adaptive behavior in autism.

Resolution of impaired multisensory processing in autism and the cost of switching sensory modality.

Children with autism spectrum disorders (ASD) exhibit alterations in multisensory processing, which may contribute to the prevalence of social and communicative deficits in this population. Resolution of multisensory deficits has been observed in teenagers with ASD for complex, social speech stimuli; however, whether this resolution extends to more basic multisensory processing deficits remains unclear. Here, in a cohort of 364 participants we show using simple, non-social audiovisual stimuli that deficits in multisensory processing observed in high-functioning children and teenagers with ASD are not evident in adults with the disorder. Computational modelling indicated that multisensory processing transitions from a default state of competition to one of facilitation, and that this transition is delayed in ASD. Further analysis revealed group differences in how sensory channels are weighted, and how this is impacted by preceding cross-sensory inputs. Our findings indicate that there is a complex and dynamic interplay among the sensory systems that differs considerably in individuals with ASD.

Seeing a talking face matters: The relationship between cortical tracking of continuous auditory-visual speech and gaze behaviour in infants, children and adults.

An auditory-visual speech benefit, the benefit that visual speech cues bring to auditory speech perception, is experienced from early on in infancy and continues to be experienced to an increasing degree with age. While there is both behavioural and neurophysiological evidence for children and adults, only behavioural evidence exists for infants - as no neurophysiological study has provided a comprehensive examination of the auditory-visual speech benefit in infants. It is also surprising that most studies on auditory-visual speech benefit do not concurrently report looking behaviour especially since the auditory-visual speech benefit rests on the assumption that listeners attend to a speaker's talking face and that there are meaningful individual differences in looking behaviour. To address these gaps, we simultaneously recorded electroencephalographic (EEG) and eye-tracking data of 5-month-olds, 4-year-olds and adults as they were presented with a speaker in auditory-only (AO), visual-only (VO), and auditory-visual (AV) modes. Cortical tracking analyses that involved forward encoding models of the speech envelope revealed that there was an auditory-visual speech benefit [i.e., AV > (A + V)], evident in 5-month-olds and adults but not 4-year-olds. Examination of cortical tracking accuracy in relation to looking behaviour, showed that infants' relative attention to the speaker's mouth (vs. eyes) was positively correlated with cortical tracking accuracy of VO speech, whereas adults' attention to the display overall was negatively correlated with cortical tracking accuracy of VO speech. This study provides the first neurophysiological evidence of auditory-visual speech benefit in infants and our results suggest ways in which current models of speech processing can be fine-tuned.

Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research.

Cognitive neuroscience, in particular research on speech and language, has seen an increase in the use of linear modeling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits under more naturalistic conditions. However, studying clinical (and often highly heterogeneous) cohorts introduces an added layer of complexity to such modeling procedures, potentially leading to instability of such techniques and, as a result, inconsistent findings. Here, we outline some key methodological considerations for applied research, referring to a hypothetical clinical experiment involving speech processing and worked examples of simulated electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing, stimulus feature extraction, model design, model training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate the implementation of each step in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied research. In doing so, we hope to provide better intuition on these more technical points and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically rich stimuli.

Neurophysiological Indices of Audiovisual Speech Processing Reveal a Hierarchy of Multisensory Integration Effects.

Seeing a speaker's face benefits speech comprehension, especially in challenging listening conditions. This perceptual benefit is thought to stem from the neural integration of visual and auditory speech at multiple stages of processing, whereby movement of a speaker's face provides temporal cues to auditory cortex, and articulatory information from the speaker's mouth can aid recognizing specific linguistic units (e.g., phonemes, syllables). However, it remains unclear how the integration of these cues varies as a function of listening conditions. Here, we sought to provide insight on these questions by examining EEG responses in humans (males and females) to natural audiovisual (AV), audio, and visual speech in quiet and in noise. We represented our speech stimuli in terms of their spectrograms and their phonetic features and then quantified the strength of the encoding of those features in the EEG using canonical correlation analysis (CCA). The encoding of both spectrotemporal and phonetic features was shown to be more robust in AV speech responses than what would have been expected from the summation of the audio and visual speech responses, suggesting that multisensory integration occurs at both spectrotemporal and phonetic stages of speech processing. We also found evidence to suggest that the integration effects may change with listening conditions; however, this was an exploratory analysis and future work will be required to examine this effect using a within-subject design. These findings demonstrate that integration of audio and visual speech occurs at multiple stages along the speech processing hierarchy.SIGNIFICANCE STATEMENT During conversation, visual cues impact our perception of speech. Integration of auditory and visual speech is thought to occur at multiple stages of speech processing and vary flexibly depending on the listening conditions. Here, we examine audiovisual (AV) integration at two stages of speech processing using the speech spectrogram and a phonetic representation, and test how AV integration adapts to degraded listening conditions. We find significant integration at both of these stages regardless of listening conditions. These findings reveal neural indices of multisensory interactions at different stages of processing and provide support for the multistage integration framework.

Operating in a Multisensory Context: Assessing the Interplay Between Multisensory Reaction Time Facilitation and Inter-sensory Task-switching Effects.

Individuals respond faster to presentations of bisensory stimuli (e.g. audio-visual targets) than to presentations of either unisensory constituent in isolation (i.e. to the auditory-alone or visual-alone components of an audio-visual stimulus). This well-established multisensory speeding effect, termed the redundant signals effect (RSE), is not predicted by simple linear summation of the unisensory response time probability distributions. Rather, the speeding is typically faster than this prediction, leading researchers to ascribe the RSE to a so-called co-activation account. According to this account, multisensory neural processing occurs whereby the unisensory inputs are integrated to produce more effective sensory-motor activation. However, the typical paradigm used to test for RSE involves random sequencing of unisensory and bisensory inputs in a mixed design, raising the possibility of an alternate attention-switching account. This intermixed design requires participants to switch between sensory modalities on many task trials (e.g. from responding to a visual stimulus to an auditory stimulus). Here we show that much, if not all, of the RSE under this paradigm can be attributed to slowing of reaction times to unisensory stimuli resulting from modality switching, and is not in fact due to speeding of responses to AV stimuli. As such, the present data do not support a co-activation account, but rather suggest that switching and mixing costs akin to those observed during classic task-switching paradigms account for the observed RSE.

Cortical Measures of Phoneme-Level Speech Encoding Correlate with the Perceived Clarity of Natural Speech.

In real-world environments, humans comprehend speech by actively integrating prior knowledge (P) and expectations with sensory input. Recent studies have revealed effects of prior information in temporal and frontal cortical areas and have suggested that these effects are underpinned by enhanced encoding of speech-specific features, rather than a broad enhancement or suppression of cortical activity. However, in terms of the specific hierarchical stages of processing involved in speech comprehension, the effects of integrating bottom-up sensory responses and top-down predictions are still unclear. In addition, it is unclear whether the predictability that comes with prior information may differentially affect speech encoding relative to the perceptual enhancement that comes with that prediction. One way to investigate these issues is through examining the impact of P on indices of cortical tracking of continuous speech features. Here, we did this by presenting participants with degraded speech sentences that either were or were not preceded by a clear recording of the same sentences while recording non-invasive electroencephalography (EEG). We assessed the impact of prior information on an isolated index of cortical tracking that reflected phoneme-level processing. Our findings suggest the possibility that prior information affects the early encoding of natural speech in a dual manner. Firstly, the availability of prior information, as hypothesized, enhanced the perceived clarity of degraded speech, which was positively correlated with changes in phoneme-level encoding across subjects. In addition, P induced an overall reduction of this cortical measure, which we interpret as resulting from the increase in predictability.

Electrophysiological Correlates of Semantic Dissimilarity Reflect the Comprehension of Natural, Narrative Speech.

People routinely hear and understand speech at rates of 120-200 words per minute [1, 2]. Thus, speech comprehension must involve rapid, online neural mechanisms that process words' meanings in an approximately time-locked fashion. However, electrophysiological evidence for such time-locked processing has been lacking for continuous speech. Although valuable insights into semantic processing have been provided by the "N400 component" of the event-related potential [3-6], this literature has been dominated by paradigms using incongruous words within specially constructed sentences, with less emphasis on natural, narrative speech comprehension. Building on the discovery that cortical activity "tracks" the dynamics of running speech [7-9] and psycholinguistic work demonstrating [10-12] and modeling [13-15] how context impacts on word processing, we describe a new approach for deriving an electrophysiological correlate of natural speech comprehension. We used a computational model [16] to quantify the meaning carried by words based on how semantically dissimilar they were to their preceding context and then regressed this measure against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time lag of 200-600 ms on centro-parietal EEG channels, characteristics common to the N400. Applying this approach to EEG datasets involving time-reversed speech, cocktail party attention, and audiovisual speech-in-noise demonstrated that this response was very sensitive to whether or not subjects understood the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion.

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

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.

Eye Can Hear Clearly Now: Inverse Effectiveness in Natural Audiovisual Speech Processing Relies on Long-Term Crossmodal Temporal Integration.

UNLABELLED: Speech comprehension is improved by viewing a speaker's face, especially in adverse hearing conditions, a principle known as inverse effectiveness. However, the neural mechanisms that help to optimize how we integrate auditory and visual speech in such suboptimal conversational environments are not yet fully understood. Using human EEG recordings, we examined how visual speech enhances the cortical representation of auditory speech at a signal-to-noise ratio that maximized the perceptual benefit conferred by multisensory processing relative to unisensory processing. We found that the influence of visual input on the neural tracking of the audio speech signal was significantly greater in noisy than in quiet listening conditions, consistent with the principle of inverse effectiveness. Although envelope tracking during audio-only speech was greatly reduced by background noise at an early processing stage, it was markedly restored by the addition of visual speech input. In background noise, multisensory integration occurred at much lower frequencies and was shown to predict the multisensory gain in behavioral performance at a time lag of ∼250 ms. Critically, we demonstrated that inverse effectiveness, in the context of natural audiovisual (AV) speech processing, relies on crossmodal integration over long temporal windows. Our findings suggest that disparate integration mechanisms contribute to the efficient processing of AV speech in background noise. SIGNIFICANCE STATEMENT: The behavioral benefit of seeing a speaker's face during conversation is especially pronounced in challenging listening environments. However, the neural mechanisms underlying this phenomenon, known as inverse effectiveness, have not yet been established. Here, we examine this in the human brain using natural speech-in-noise stimuli that were designed specifically to maximize the behavioral benefit of audiovisual (AV) speech. We find that this benefit arises from our ability to integrate multimodal information over longer periods of time. Our data also suggest that the addition of visual speech restores early tracking of the acoustic speech signal during excessive background noise. These findings support and extend current mechanistic perspectives on AV speech perception.

Visual Cortical Entrainment to Motion and Categorical Speech Features during Silent Lipreading.

Speech is a multisensory percept, comprising an auditory and visual component. While the content and processing pathways of audio speech have been well characterized, the visual component is less well understood. In this work, we expand current methodologies using system identification to introduce a framework that facilitates the study of visual speech in its natural, continuous form. Specifically, we use models based on the unheard acoustic envelope (E), the motion signal (M) and categorical visual speech features (V) to predict EEG activity during silent lipreading. Our results show that each of these models performs similarly at predicting EEG in visual regions and that respective combinations of the individual models (EV, MV, EM and EMV) provide an improved prediction of the neural activity over their constituent models. In comparing these different combinations, we find that the model incorporating all three types of features (EMV) outperforms the individual models, as well as both the EV and MV models, while it performs similarly to the EM model. Importantly, EM does not outperform EV and MV, which, considering the higher dimensionality of the V model, suggests that more data is needed to clarify this finding. Nevertheless, the performance of EMV, and comparisons of the subject performances for the three individual models, provides further evidence to suggest that visual regions are involved in both low-level processing of stimulus dynamics and categorical speech perception. This framework may prove useful for investigating modality-specific processing of visual speech under naturalistic conditions.

Congruent Visual Speech Enhances Cortical Entrainment to Continuous Auditory Speech in Noise-Free Conditions.

Congruent audiovisual speech enhances our ability to comprehend a speaker, even in noise-free conditions. When incongruent auditory and visual information is presented concurrently, it can hinder a listener's perception and even cause him or her to perceive information that was not presented in either modality. Efforts to investigate the neural basis of these effects have often focused on the special case of discrete audiovisual syllables that are spatially and temporally congruent, with less work done on the case of natural, continuous speech. Recent electrophysiological studies have demonstrated that cortical response measures to continuous auditory speech can be easily obtained using multivariate analysis methods. Here, we apply such methods to the case of audiovisual speech and, importantly, present a novel framework for indexing multisensory integration in the context of continuous speech. Specifically, we examine how the temporal and contextual congruency of ongoing audiovisual speech affects the cortical encoding of the speech envelope in humans using electroencephalography. We demonstrate that the cortical representation of the speech envelope is enhanced by the presentation of congruent audiovisual speech in noise-free conditions. Furthermore, we show that this is likely attributable to the contribution of neural generators that are not particularly active during unimodal stimulation and that it is most prominent at the temporal scale corresponding to syllabic rate (2-6 Hz). Finally, our data suggest that neural entrainment to the speech envelope is inhibited when the auditory and visual streams are incongruent both temporally and contextually. SIGNIFICANCE STATEMENT: Seeing a speaker's face as he or she talks can greatly help in understanding what the speaker is saying. This is because the speaker's facial movements relay information about what the speaker is saying, but also, importantly, when the speaker is saying it. Studying how the brain uses this timing relationship to combine information from continuous auditory and visual speech has traditionally been methodologically difficult. Here we introduce a new approach for doing this using relatively inexpensive and noninvasive scalp recordings. Specifically, we show that the brain's representation of auditory speech is enhanced when the accompanying visual speech signal shares the same timing. Furthermore, we show that this enhancement is most pronounced at a time scale that corresponds to mean syllable length.

The cortical representation of the speech envelope is earlier for audiovisual speech than audio speech.

Visual speech can greatly enhance a listener's comprehension of auditory speech when they are presented simultaneously. Efforts to determine the neural underpinnings of this phenomenon have been hampered by the limited temporal resolution of hemodynamic imaging and the fact that EEG and magnetoencephalographic data are usually analyzed in response to simple, discrete stimuli. Recent research has shown that neuronal activity in human auditory cortex tracks the envelope of natural speech. Here, we exploit this finding by estimating a linear forward-mapping between the speech envelope and EEG data and show that the latency at which the envelope of natural speech is represented in cortex is shortened by >10 ms when continuous audiovisual speech is presented compared with audio-only speech. In addition, we use a reverse-mapping approach to reconstruct an estimate of the speech stimulus from the EEG data and, by comparing the bimodal estimate with the sum of the unimodal estimates, find no evidence of any nonlinear additive effects in the audiovisual speech condition. These findings point to an underlying mechanism that could account for enhanced comprehension during audiovisual speech. Specifically, we hypothesize that low-level acoustic features that are temporally coherent with the preceding visual stream may be synthesized into a speech object at an earlier latency, which may provide an extended period of low-level processing before extraction of semantic information.

Endogenous auditory frequency-based attention modulates electroencephalogram-based measures of obligatory sensory activity in humans.

Auditory selective attention is the ability to enhance the processing of a single sound source, while simultaneously suppressing the processing of other competing sound sources. Recent research has addressed a long-running debate by showing that endogenous attention produces effects on obligatory sensory responses to continuous and competing auditory stimuli. However, until now, this result has only been shown under conditions where the competing stimuli differed in both their frequency characteristics and, importantly, their spatial location. Thus, it is unknown whether endogenous selective attention based only on nonspatial features modulates obligatory sensory processing. Here, we investigate this issue using a diotic paradigm, such that competing auditory stimuli differ in frequency, but had no separation in space. We find a significant effect of attention on electroencephalogram-based measures of obligatory sensory processing at several poststimulus latencies. We discuss these results in terms of previous research on feature-based attention and by comparing our findings with the previous work using stimuli that differed both in terms of spatial and frequency-based characteristics.

The effects of attention and visual input on the representation of natural speech in EEG.

Traditionally, the use of electroencephalography (EEG) to study the neural processing of natural stimuli in humans has been hampered by the need to repeatedly present discrete stimuli. Progress has been made recently by the realization that cortical population activity tracks the amplitude envelope of speech stimuli. This has led to studies using linear regression methods which allow the presentation of continuous speech. One such method, known as stimulus reconstruction, has so far only been utilized in multi-electrode cortical surface recordings and magnetoencephalography (MEG). Here, in two studies, we show that such an approach is also possible with EEG, despite the poorer signal-to-noise ratio of the data. In the first study, we show that it is possible to decode attention in a naturalistic cocktail party scenario on a single trial (≈60 s) basis. In the second, we show that the representation of the envelope of auditory speech in the cortex is more robust when accompanied by visual speech. The sensitivity of this inexpensive, widely-accessible technology for the online monitoring of natural stimuli has implications for the design of future studies of the cocktail party problem and for the implementation of EEG-based brain-computer interfaces.