Systematic biases in early ERP and ERF components as a result of high-pass filtering

An extremely fast neural mechanism to detect emotional visual stimuli: A two-experiment study

Defining the brain mechanisms underlying initial emotional evaluation is a key but unexplored clue to understanding affective processing. Event-related potentials (ERPs), especially suited for investigating this issue, were recorded in two experiments (n = 36 and n = 35). We presented emotionally negative (spiders) and neutral (wheels) silhouettes homogenized regarding their visual parameters. In Experiment 1, stimuli appeared at fixation or in the periphery (200 trials per condition and location), the former eliciting a N40 (39 milliseconds) and a P80 (or C1: 80 milliseconds) component, and the latter only a P80. In Experiment 2, stimuli were presented only at fixation (500 trials per condition). Again, an N40 (45 milliseconds) was observed, followed by a P100 (or P1: 105 milliseconds). Analyses revealed significantly greater N40-C1P1 peak-to-peak amplitudes for spiders in both experiments, and ANCOVAs showed that these effects were not explained by C1P1 alone, but that processes underlying N40 significantly contributed. Source analyses pointed to V1 as an N40 focus (more clearly in Experiment 2). Sources for C1P1 included V1 (P80) and V2/LOC (P80 and P100). These results and their timing point to low-order structures (such as visual thalamic nuclei or superior colliculi) or the visual cortex itself, as candidates for initial evaluation structures.

Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

Exploring the Interplay Between Language Comprehension and Cortical Tracking: The Bilingual Test Case

Cortical tracking, the synchronization of brain activity to linguistic rhythms is a well-established phenomenon. However, its nature has been heavily contested: Is it purely epiphenomenal or does it play a fundamental role in speech comprehension? Previous research has used intelligibility manipulations to examine this topic. Here, we instead varied listeners' language comprehension skills while keeping the auditory stimulus constant. To do so, we tested 22 native English speakers and 22 Spanish/Catalan bilinguals learning English as a second language (SL) in an EEG cortical entrainment experiment and correlated the responses with the magnitude of the N400 component of a semantic comprehension task. As expected, native listeners effectively tracked sentential, phrasal, and syllabic linguistic structures. In contrast, SL listeners exhibited limitations in tracking sentential structures but successfully tracked phrasal and syllabic rhythms. Importantly, the amplitude of the neural entrainment correlated with the amplitude of the detection of semantic incongruities in SLs, showing a direct connection between tracking and the ability to understand speech. Together, these findings shed light on the interplay between language comprehension and cortical tracking, to identify neural entrainment as a fundamental principle for speech comprehension.

Optimal filters for ERP research I: A general approach for selecting filter settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Optimal filters for ERP research II: Recommended settings for seven common ERP components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring methods, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for data sets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

Attention modulates early visual processing: An association between subjective contrast perception and early C1 ERP component

The question of whether spatial attention can modulate initial afferent activity in area V1, as measured by the earliest visual event-related potential (ERP) component "C1", is still the subject of debate. Because attention always enhances behavioral performance, previous research has focused on finding evidence of attention-related enhancements in visual neural responses. However, recent psychophysical studies revealed a complex picture of attention's influence on visual perception: attention amplifies the perceived contrast of low-contrast stimuli while dampening the perceived contrast of high-contrast stimuli. This evidence suggests that attention may not invariably augment visual neural responses but could instead exert inhibitory effects under certain circumstances. Whether this bi-directional modulation of attention also manifests in C1 and whether the modulation of C1 underpins the attentional influence on contrast perception remain unknown. To address these questions, we conducted two experiments (N = 67 in total) by employing a combination of behavioral and ERP methodologies. Our results did not unveil a uniform attentional enhancement or attenuation effect of C1 across all subjects. However, an intriguing correlation between the attentional effects of C1 and contrast appearance for high-contrast stimuli did emerge, revealing an association between attentional modulation of C1 and the attentional modulation of contrast appearance. This finding offers new insights into the relationship between attention, perceptual experience, and early visual neural processing, suggesting that the attentional effect on subjective visual perception could be mediated by the attentional modulation of the earliest visual cortical response.

A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG

The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals. Then, we verified the consistency of optically-pumped magnetometer and electroencephalogram in detecting N400 using mutual information index. Multivariate pattern analysis revealed the difference in decoding performance of these two modalities, which can be further validated by dynamic/stable coding analysis on the temporal generalization matrix. The results from searchlight analysis provided a neural basis for this dissimilarity at the magnetoencephalography source level and the electroencephalogram sensor level. This study opens a new avenue for investigating the brain's coding patterns using wearable magnetoencephalography and reveals the differences in sensitivity between the two modalities in reflecting neuron representation patterns.

Dissociating the pre-activation of word meaning and form during sentence comprehension: Evidence from EEG representational similarity analysis

During language comprehension, the processing of each incoming word is facilitated in proportion to its predictability. Here, we asked whether anticipated upcoming linguistic information is actually pre-activated before new bottom-up input becomes available, and if so, whether this pre-activation is limited to the level of semantic features, or whether extends to representations of individual word-forms (orthography/phonology). We carried out Representational Similarity Analysis on EEG data while participants read highly constraining sentences. Prior to the onset of the expected target words, sentence pairs predicting semantically related words (financial "bank" - "loan") and form-related words (financial "bank" - river "bank") produced more similar neural patterns than pairs predicting unrelated words ("bank" - "lesson"). This provides direct neural evidence for item-specific semantic and form predictive pre-activation. Moreover, the semantic pre-activation effect preceded the form pre-activation effect, suggesting that top-down pre-activation is propagated from higher to lower levels of the linguistic hierarchy over time.

Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients

Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.

Contralateral delay activity and alpha lateralization reflect retinotopic and screen-centered reference frames in visual memory

The visual system represents objects in a lateralized manner, with contralateral cortical hemispheres responsible for left and right visual hemifields. This organization extends to visual short-term memory (VSTM), as evidenced by electrophysiological indices of VSTM maintenance: contralateral delay activity (CDA) and alpha-band lateralization. However, it remains unclear if VSTM represents object locations in gaze-centered (retinotopic) or screen-centered (spatiotopic) coordinates, especially after eye movements. In two experiments, participants encoded the colors of target objects and made a lateral saccade during the maintenance interval, thereby shifting the object's location on the retina. A non-lateralized probe stimulus was then presented at the new fixation for a change detection task. The CDA maintained lateralization towards the target's original retinotopic location, unaffected by subsequent saccades, and did not invert polarity even when a saccade brought that location into the opposite hemifield. We also found conventional alpha lateralization towards the target's location before a saccade. After a saccade, however, alpha was lateralized towards the screen center regardless of the target's original location, even in a control condition without any memory requirements. This suggests that post-saccadic alpha-band lateralization reflects attentional processes unrelated to memory, while pre- and post-saccade CDA reflect VSTM maintenance in a retinotopic reference frame.

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Saccade execution increases the preview effect with faces: An EEG and eye-tracking coregistration study

Under naturalistic viewing conditions, humans conduct about three to four saccadic eye movements per second. These dynamics imply that in real life, humans rarely see something completely new; there is usually a preview of the upcoming foveal input from extrafoveal regions of the visual field. In line with results from the field of reading research, we have shown with EEG and eye-tracking coregistration that an extrafoveal preview also affects postsaccadic visual object processing and facilitates discrimination. Here, we ask whether this preview effect in the fixation-locked N170, and in manual responses to the postsaccadic target face (tilt discrimination), requires saccade execution. Participants performed a gaze-contingent experiment in which extrafoveal face images could change their orientation during a saccade directed to them. In a control block, participants maintained stable gaze throughout the experiment and the extrafoveal face reappeared foveally after a simulated saccade latency. Compared with this no-saccade condition, the neural and the behavioral preview effects were much larger in the saccade condition. We also found shorter first fixation durations after an invalid preview, which is in contrast to reading studies. We interpret the increased preview effect under saccade execution as the result of the additional sensorimotor processes that come with gaze behavior compared with visual perception under stable fixation. In addition, our findings call into question whether EEG studies with fixed gaze capture key properties and dynamics of active, natural vision.

Effect of presentation rate on auditory processing in Rett syndrome: event-related potential study

BACKGROUND

Rett syndrome (RS) is a rare neurodevelopmental disorder characterized by mutations in the MECP2 gene. Patients with RS have severe motor abnormalities and are often unable to walk, use hands and speak. The preservation of perceptual and cognitive functions is hard to assess, while clinicians and care-givers point out that these patients need more time to process information than typically developing peers. Neurophysiological correlates of auditory processing have been also found to be distorted in RS, but sound presentation rates were relatively quick in these studies (stimulus onset asynchrony, SOA < 1000 ms). As auditory event-related potential (ERP) is typically increased with prolongation of SOA we aim to study if SOA prolongation might compensate for observed abnormalities.

METHODS

We presented a repetitive stimulus (1000 Hz) at three different SOAs of 900 ms, 1800 ms, and 3600 ms in children with RS (N = 24, Mean age = 9.0 ± 3.1) and their typical development (TD) peers (N = 27, Mean age = 9.7 ± 3.4) while recording 28-channels electroencephalogram, EEG. Some RS participants (n = 10) did not show clear ERP and were excluded from the analysis.

RESULTS

Major ERP components (here assessed as N1P1 and P2N1 peak-to-peak values) were smaller at SOA 900 than at longer SOAs in both groups, pointing out that the basic mechanism of adaptation in the auditory system is preserved in at least in RS patients with evident ERPs. At the same time the latencies of these components were significantly delayed in the RS than in TD. Moreover, late components (P2N1 and N2P2) were drastically reduced in Rett syndrome irrespective of the SOA, suggesting a largely affected mechanism of integration of upcoming sensory input with memory. Moreover, developmental stagnation of auditory ERP characterized patients with RS: absence of typical P2N1 enlargement and P1 and N1 shortening with age at least for shortest SOA.

LIMITATIONS

We could not figure out the cause for the high percentage of no-evident ERP RS participants and our final sample of the RS group was rather small. Also, our study did not include a control clinical group.

CONCLUSIONS

Thus, auditory ERPs inform us about abnormalities within auditory processing that cannot be fully overcomed by slowing presentation rate.

Optimal Filters for ERP Research II: Recommended Settings for Seven Common ERP Components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring method, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for datasets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

Illusory object recognition is either perceptual or cognitive in origin depending on decision confidence

We occasionally misinterpret ambiguous sensory input or report a stimulus when none is presented. It is unknown whether such errors have a sensory origin and reflect true perceptual illusions, or whether they have a more cognitive origin (e.g., are due to guessing), or both. When participants performed an error-prone and challenging face/house discrimination task, multivariate electroencephalography (EEG) analyses revealed that during decision errors (e.g., mistaking a face for a house), sensory stages of visual information processing initially represent the presented stimulus category. Crucially however, when participants were confident in their erroneous decision, so when the illusion was strongest, this neural representation flipped later in time and reflected the incorrectly reported percept. This flip in neural pattern was absent for decisions that were made with low confidence. This work demonstrates that decision confidence arbitrates between perceptual decision errors, which reflect true illusions of perception, and cognitive decision errors, which do not.

Visual continuous recognition reveals behavioral and neural differences for short- and long-term scene memory

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory at timescales relevant to working and longer-term memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 s) or longer-term intervals of between 30 s and 3 min or 4 and 10 min. Overall recognition was far above chance, with better performance at shorter- than longer-term intervals. Sensor-level ANOVA and post hoc pairwise comparisons of event related potentials (ERPs) revealed three main findings: (1) occipital and parietal amplitudes distinguishing new and old from scrambled scenes; (2) frontal amplitudes distinguishing old from new scenes with a central positivity highest for hits compared to misses, false alarms and correct rejections; and (3) frontal and parietal changes from ∼300 to ∼600 ms distinguishing among old scenes previously encountered at short- and longer-term retention intervals. These findings reveal how distributed spatiotemporal neural changes evolve to support short- and longer-term recognition of complex scenes.

The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities

Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.

All-or-none neural mechanisms underlying face categorization: evidence from the N170

Categorization of visual stimuli is an intrinsic aspect of human perception. Whether the cortical mechanisms underlying categorization operate in an all-or-none or graded fashion remains unclear. In this study, we addressed this issue in the context of the face-specific N170. Specifically, we investigated whether N170 amplitudes grade with the amount of face information available in an image, or a full response is generated whenever a face is perceived. We employed linear mixed-effects modeling to inspect the dependency of N170 amplitudes on stimulus properties and duration, and their relationships to participants' subjective perception. Consistent with previous studies, we found a stronger N170 evoked by faces presented for longer durations. However, further analysis with equivalence tests revealed that this duration effect was eliminated when only faces perceived with high confidence were considered. Therefore, previous evidence supporting the graded hypothesis is more likely to be an artifact of mixing heterogeneous "all" and "none" trial types in signal averaging. These results support the hypothesis that the N170 is generated in an all-or-none manner and, by extension, suggest that categorization of faces may follow a similar pattern.

Interference suppression techniques for OPM-based MEG: Opportunities and challenges

One of the primary technical challenges facing magnetoencephalography (MEG) is that the magnitude of neuromagnetic fields is several orders of magnitude lower than interfering signals. Recently, a new type of sensor has been developed - the optically pumped magnetometer (OPM). These sensors can be placed directly on the scalp and move with the head during participant movement, making them wearable. This opens up a range of exciting experimental and clinical opportunities for OPM-based MEG experiments, including paediatric studies, and the incorporation of naturalistic movements into neuroimaging paradigms. However, OPMs face some unique challenges in terms of interference suppression, especially in situations involving mobile participants, and when OPMs are integrated with electrical equipment required for naturalistic paradigms, such as motion capture systems. Here we briefly review various hardware solutions for OPM interference suppression. We then outline several signal processing strategies aimed at increasing the signal from neuromagnetic sources. These include regression-based strategies, temporal filtering and spatial filtering approaches. The focus is on the practical application of these signal processing algorithms to OPM data. In a similar vein, we include two worked-through experiments using OPM data collected from a whole-head sensor array. These tutorial-style examples illustrate how the steps for suppressing external interference can be implemented, including the associated data and code so that researchers can try the pipelines for themselves. With the popularity of OPM-based MEG rising, there will be an increasing need to deal with interference suppression. We hope this practical paper provides a resource for OPM-based MEG researchers to build upon.

NEAR: An artifact removal pipeline for human newborn EEG data

Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR.

The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area-the sensorimotor area.

Comparing Different Filter-Parameter for Pre-Processing of Brain-Stimulation Evoked Motor Potentials

Background: Brain stimulation motor-evoked potentials (MEPs) are transient signals and not periodic signals, and thus, they differ significantly in their properties from classical surface electromyograms. Unsuitable pre-processing of MEPs due to inappropriate filter settings leads to distortions. Filtering of extensor carpi radialis MEPs with transient signal characteristics of 20 subjects was examined. The effects of a 1st-order Butterworth high-pass filter (HPF) with different cut-off frequencies 1 Hz, 20 Hz, 40 Hz, and 80 Hz and a 5 Hz Butterworth high-pass filter with degrees 1st, 2nd, 4th, 8th-order are investigated for the filter output. Results: The filtering of the MEPs with an inappropriate filter setting led to distortions on the parameters peak-to-peak amplitudes of the MEP (MEP_pp) and the absolute integral of the MEP (MEP_area). The lowest distortions of all of the examined filter parameters were revealed after filtering with the lowest filter order and the lowest cut-off frequency. The 1st-order 1 Hz HPF calculation results in a difference of −0.53% (p < 0.001) for the MEPpp and −1.94% (p < 0.001) for the MEParea. Significance: Reproducibility is a major concern in science, including brain stimulation research. Only the filtering of the MEPs with appropriate filter settings led to mostly undistorted MEPs.

Modulation of the Earliest Component of the Human VEP by Spatial Attention: An Investigation of Task Demands

Spatial attention modulations of initial afferent activity in area V1, indexed by the first component “C1” of the human visual evoked potential, are rarely found. It has thus been suggested that early modulation is induced only by special task conditions, but what these conditions are remains unknown. Recent failed replications—findings of no C1 modulation using a certain task that had previously produced robust modulations—present a strong basis for examining this question. We ran 3 experiments, the first to more exactly replicate the stimulus and behavioral conditions of the original task, and the second and third to manipulate 2 key factors that differed in the failed replication studies: the provision of informative performance feedback, and the degree to which the probed stimulus features matched those facilitating target perception. Although there was an overall significant C1 modulation of 11%, individually, only experiments 1 and 2 showed reliable effects, underlining that the modulations do occur but not consistently. Better feedback induced greater P1, but not C1, modulations. Target-probe feature matching had an inconsistent influence on modulation patterns, with behavioral performance differences and signal-overlap analyses suggesting interference from extrastriate modulations as a potential cause.

Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-threshold region, and pre-processing libraries for universal EEG-based datasets.

Early reduction of sensory processing within the visual cortex when switching from internal to external attention

The neurocognitive process underlying attention switches between external (perception-based) and internal (memory-based) attention is poorly characterized. Previous research has found that when participants switch attention either between two perception-based tasks (within-domain switches) or between a memory- and a perception-based task (between-domain switches), a substantial and similar processing cost was observed compared to the repetition of the same task (Verschooren, Schindler, De Raedt, & Pourtois, 2019). Here, we recorded 64-channel EEG while participants carried out within- versus between-domain switches of attention. ERP results showed that during early sensory processing, a marked P1 attenuation was associated with both switch types, suggesting that switching was associated with an early bottleneck during information processing. This early gating effect was stronger when switching from an internal to an external task, compared to switching between external tasks, suggesting different top-down requirements for them. These findings are in line with earlier proposals in the literature.

Effects of high and low power on the visual encoding of faces

The experience of power is typically associated with social disengagement, yet power has also been shown to facilitate configural visual encoding - a process that supports the initial perception of a human face. To investigate this apparent contradiction, we directly tested whether power influences the visual encoding of faces. Two experiments, using neural and psychophysical assessments, revealed that low power impeded both first-order configural processing (the encoding of a stimulus as a face, assessed by the N170 event-related potential) and second-order configural processing (the encoding of feature distances within configuration, assessed using the face inversion paradigm), relative to high-power and control conditions. Power did not significantly affect facial feature encoding. Results reveal an early and automatic effect of low power on face perception, characterized primarily by diminished face processing. These findings suggest a novel interplay between visual and cognitive processes in power's influence on social behavior.

Standardized measurement error: A universal metric of data quality for averaged event-related potentials

Event-related potentials (ERPs) can be very noisy, and yet, there is no widely accepted metric of ERP data quality. Here, we propose a universal measure of data quality for ERP research-the standardized measurement error (SME)-which is a special case of the standard error of measurement. Whereas some existing metrics provide a generic quantification of the noise level, the SME quantifies the data quality (precision) for the specific amplitude or latency value being measured in a given study (e.g., the peak latency of the P3 wave). It can be applied to virtually any value that is derived from averaged ERP waveforms, making it a universal measure of data quality. In addition, the SME quantifies the data quality for each individual participant, making it possible to identify participants with low-quality data and "bad" channels. When appropriately aggregated across individuals, SME values can be used to quantify the combined impact of the single-trial EEG noise and the number of trials being averaged together on the effect size and statistical power in a given experiment. If SME values were regularly included in published articles, researchers could identify the recording and analysis procedures that produce the highest data quality, which could ultimately lead to increased effect sizes and greater replicability across the field.

Observation of Nociceptive Processing: Effect of Intra-Epidermal Electric Stimulus Properties on Detection Probability and Evoked Potentials

Monitoring nociceptive processing is a current challenge due to a lack of objective measures. Recently, we developed a method for simultaneous tracking of psychophysical detection probability and brain evoked potentials in response to intra-epidermal stimulation. An exploratory investigation showed that we could quantify nociceptive system behavior by estimating the effect of stimulus properties on the evoked potential (EP). The goal in this work was to accurately measure nociceptive system behavior using this method in a large group of healthy subjects to identify the locations and latencies of EP components and the effect of single- and double-pulse stimuli with an inter-pulse interval of 10 or 40 ms on these EP components and detection probability. First, we observed the effect of filter settings and channel selection on the EP. Subsequently, we compared statistical models to assess correlation of EP and detection probability with stimulus properties, and quantified the effect of stimulus properties on both outcome measures through linear mixed regression. We observed lateral and central EP components in response to intra-epidermal stimulation. Detection probability and central EP components were positively correlated to the amplitude of each pulse, regardless of the inter-pulse interval, and negatively correlated to the trial number. Both central and lateral EP components also showed strong correlation with detection. These results show that both the observed EP and the detection probability reflect the various steps of processing of a nociceptive stimulus, including peripheral nerve fiber recruitment, central synaptic summation, and habituation to a repeated stimulus.

Aberrant Frequency Related Change-Detection Activity in Chronic Tinnitus

Tinnitus is the perception of sound without the occurrence of an acoustic event. The deficit in auditory sensory or echoic memory may be the cause of the perception of tinnitus. This study considered the mismatch negativity (MMN) to investigate the potential difference between and within groups of persons with normal hearing (NH) and tinnitus. Using an auditory multi-feature paradigm to elicit the MMN, this study considered the MMN peak amplitude at two central frequencies for two MMN subcomponents. These central frequencies were 1 and 5 kHz, which the latter was closer to the perceived tinnitus frequency in the group with tinnitus. The deviants were higher frequency, lower frequency, higher intensity, lower intensity, duration, location (left), location (right), and gap. The pure tone audiometry (PTA) test and distortion product otoacoustic emissions (DPOAE) test showed no meaningful difference between the two groups. For the frontal subcomponent, the mean amplitudes of the MMN peak for the two groups illustrated less negative meaningful MMN peak amplitudes in the group of persons with tinnitus. For the supratemporal component at 5 kHz central frequency, amplitudes were lower for the group of persons with tinnitus, whereas for the central frequency of 1 kHz, most deviants exhibited meaningful differences. Additionally, within-group comparisons indicated that mean amplitudes for both groups were more negative at the central frequency of 1 kHz for the frontal MMN subcomponent. In comparison, the supratemporal component illustrated a lower peak amplitude at 5 kHz central frequency in the group of persons with tinnitus and no difference in the NH group, which is a unique observation of this study. Results of the between-groups are in accordance with previous studies and within-group comparisons consider the probability of decreasing the change detection capability of the brain. The results of this study indicate that increasing the frequency of the stimuli close to the tinnitus perceived frequencies decreases the prediction error, including the prediction error of the silence. Such a decrease may cause the prediction error of the spontaneous neural activity in the auditory pathway to exceed the silence prediction error, and as a result, increases the probability of occurrence of tinnitus in higher frequencies according to the predictive coding model.

N-Back Related ERPs Depend on Stimulus Type, Task Structure, Pre-processing, and Lab Factors

The N-Back, a common working memory (WM) updating task, is increasingly used in basic and applied psychological research. As such, an increasing number of electroencephalogram (EEG) studies have sought to identify the electrophysiological signatures of N-Back task performance. However, stimulus type, task structure, pre-processing methods, and differences in the laboratory environment, including the EEG recording setup employed, greatly vary across studies, which in turn may introduce inconsistencies in the obtained results. Here we address this issue by conducting nine different variations of an N-Back task manipulating stimulus type and task structure. Furthermore, we explored the effect of the pre-processing method used and differences in the laboratory environment. Results reveal significant differences in behavioral and electrophysiological signatures in response to N-Back stimulus type, task structure, pre-processing method, and laboratory environment. In conclusion, we suggest that experimental factors, analysis pipeline, and laboratory differences, which are often ignored in the literature, need to be accounted for when interpreting findings and making comparisons across studies.

EEG Integrated Platform Lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation

BACKGROUND

The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources.

NEW METHOD

In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters.

RESULTS

We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal.

CONCLUSIONS

The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.

The temporal evolution of pre-stimulus slow cortical potentials is associated with an upcoming stimulus' access to visual consciousness

Slow cortical potentials (SCPs) have been proposed to be essential for the formation of conscious experience. To examine their temporal characteristics, we recorded electroencephalography during a visual backward-masking task, which required participants to localize the missing part of a target stimulus. A subsequent confidence rating was used as a proxy for the target's access to consciousness. Event-related potentials (ERPs) of all correct trials were determined relative to a brief period immediately before the target and then compared among consciousness levels. In an interval ranging from 2 s prior to target presentation up to this period, a negative relationship between slowly fluctuating ERP values and the level of consciousness became evident. After target presentation, high conscious awareness was characterized by an enhanced visual awareness negativity, an increased P3 component, and associated positive SCPs. Together, these findings add new evidence to the proposed role of SCPs in the emergence of visual consciousness.

EEG correlates of physical effort and reward processing during reinforcement learning

Effort-based decision making is often modeled using subjective value, a function of reward discounted by effort. We asked whether EEG event-related potential (ERP) correlates of reward processing are also modulated by physical effort. Human participants performed a task in which they were required to accurately produce target levels of muscle activation to receive rewards. Quadriceps muscle activation was recorded with electromyography (EMG) during isometric knee extension. On a given trial, the target muscle activation required either low or high effort. The effort was determined probabilistically according to a binary choice, such that the responses were associated with 20% and 80% probability of high effort. This contingency could only be learned through experience, and it reversed periodically. Binary reinforcement feedback depended on accurately producing the target muscle activity. Participants adaptively avoided effort by switching responses more frequently after choices that resulted in hard effort. Feedback after participants' choices that revealed the resulting effort requirement did not elicit modulation of the feedback-related negativity/reward positivity (FRN/RP). However, the neural response to reinforcement outcome after effort production was increased by preceding physical effort. Source decomposition revealed separable early and late positive deflections contributing to the ERP. The main effect of reward outcome, characteristic of the FRN/RP, loaded onto the earlier component, whereas the reward × effort interaction was observed only in the later positivity, which resembled the P300. Thus, retrospective effort modulates reward processing. This may explain paradoxical behavioral findings whereby rewards requiring more effort to obtain can become more powerful reinforcers.NEW & NOTEWORTHY Choices probabilistically determined the physical effort requirements for a subsequent task, and reward depended on task performance. Feedback revealing whether choices resulted in easy or hard effort did not elicit reinforcement learning signals. However, the neural responses to reinforcement were modulated by preceding effort. Thus, effort itself was not treated as loss or punishment, but it affected the responses to subsequent reinforcement outcomes. This may explain how effort can enhance the motivational effect of reward.

Omission related brain responses reflect specific and unspecific action-effect couplings

When an auditory stimulus is predicted but unexpectedly omitted, an omission response can be observed in the EEG. This endogenous response to the absence of a stimulus demonstrates the important role of prediction in perception. SanMiguel et al. (2013a) showed that in order to observe an omission response, a specific prediction concerning the identity of an upcoming stimulus is necessary. They used button presses coupled to either a single sound (predictable identity), or a random sound (unpredictable identity). In the event-related potentials, a sequence of omission responses consisting of oN1, oN2, and oP3 was observed in the single condition but not in the random condition. Given the importance of omission studies to understand the role of prediction in perception, we replicated this study. We enhanced statistical power by doubling the sample size and adjusting data pre-processing, and applied temporal principal component analysis and replication Bayes statistics. Results in the single sound condition were successfully replicated. Principal component analysis additionally revealed attenuated oN1 and oP3 omission responses in the random sound condition. These results suggest the existence of both specific and unspecific predictions along the sound processing hierarchy, where precision weighting possibly influences the strength of prediction error. Results are discussed in the framework of predictive coding and are congruent with everyday life, where uncertainty often requires broader or more general predictions.

Neurophysiological correlates of emotional face perception consciousness

Aim of the present study was to investigate the neurophysiologic correlates of the conscious and not conscious perception of faces (presented for 14, 40, 80 ms) with happy and sad emotional valence. Electroencephalographic data of 22 participants during a report-based visual task were recorded. Both happy and sad faces presented for 14 ms showed a longer N170 latency compared to the faces presented for 40 and 80 ms. A shorter latency of early components (before N170) was found in the right hemisphere and a longer latency in the left one in response to the happy faces presented for 14 ms compared to those presented for longer times. The faces presented for 14 ms, declared as consciously perceived, evoked a higher brain response compared to those declared as not perceived. Parietal and cingulate brain areas showed a lower intensity of the brain response to the consciously perceived faces in the early components. Happy faces showed a greater brain response when consciously detected, while the sad faces induced a greater brain response when not consciously detected. The findings suggest that the N170 may be the epiphenomenon of an earlier consciously detection. Moreover, these preliminary results seem to support a main role of parietal and cingulate brain areas into not conscious perception.

What exactly is missing here? The sensory processing of unpredictable omissions is modulated by the specificity of expected action-effects

We select our actions according to the desired outcomes; for instance, piano players press certain keys to generate specific musical notes. It is well-described that the omission of a predicted action-effect may elicit prediction error signals in the brain, but what happens in the case of simultaneous effector-specific (by contrast to effector-unspecific) predictions? To answer this question, we asked participants to press left and right keys to generate tones A and B; based on the action-effect association, the tones' identity was either predictable or unpredictable, while rarely, the expected input was omitted. Crucially, the data show that omissions following hand-specific associations reliably elicited a late omission N1 (oN1) component, by contrast to the hand-unspecific associations, where the late oN1 was rather weak. An additional condition where both key-presses generated a unique tone was implemented. Here, rare omissions of the expected tone generated both early and late oN1 responses, by contrast to the condition in which two simultaneous action-effect representations had to be maintained, where only late oN1 responses were elicited. Finally, omission P3 (oP3) responses were strongly elicited for all omission types without differences, indicating that a general expectation based on a tone presentation (rather than which tone), is likely indexed at this stage. The present results emphasize the top-down effects of action intention on the sensory processing of omissions, where unspecific (vs. specific) and multiple (vs. single) action-effect representations are associated with processing costs at the early sensory levels.

Unconstrained multivariate EEG decoding can help detect lexical-semantic processing in individual children

In conditions such as minimally-verbal autism, standard assessments of language comprehension are often unreliable. Given the known heterogeneity within the autistic population, it is crucial to design tests of semantic comprehension that are sensitive in individuals. Recent efforts to develop neural signals of language comprehension have focused on the N400, a robust marker of lexical-semantic violation at the group level. However, homogeneity of response in individual neurotypical children has not been established. Here, we presented 20 neurotypical children with congruent and incongruent visual animations and spoken sentences while measuring their neural response using electroencephalography (EEG). Despite robust group-level responses, we found high inter-individual variability in response to lexico-semantic anomalies. To overcome this, we analysed our data using temporally and spatially unconstrained multivariate pattern analyses (MVPA), supplemented by descriptive analyses to examine the timecourse, topography, and strength of the effect. Our results show that neurotypical children exhibit heterogenous responses to lexical-semantic violation, implying that any application to heterogenous disorders such as autism spectrum disorder will require individual-subject analyses that are robust to variation in topology and timecourse of neural responses.

Neural oscillations and event-related potentials reveal how semantic congruence drives long-term memory in both young and older humans

Long-term memory can improve when incoming information is congruent with known semantic information. This so-called congruence effect has widely been shown in younger adults, but age-related changes and neural mechanisms remain unclear. Here, congruence improved recognition memory in younger and older adults (i.e. congruence effect), with only weak evidence for age-related decline in one behavioral study. In an EEG study, however, no significant behavioral differences in the congruence effect could be observed between age-groups. In line with this observation, electroencephalography data show that, in both groups, congruence led to widespread differences in Event-Related Potentials (ERPs), starting at around 400 ms after stimulus onset, and theta, alpha and beta oscillations (4-20 Hz). Importantly, these congruence-related ERPs were associated to increases in memory performance for congruent items, in both age groups. Finally, the described ERPs and neural oscillations in the theta-alpha range (5-13 Hz) were less pronounced in the elderly despite a preserved congruence effect. Together, semantic congruence increases long-term memory across the lifespan, and, at the neural level, this could be linked to neural oscillations in the theta, alpha and beta range, as well as ERPs that were previously associated with semantic processing.

Valence-dependent Disruption in Processing of Facial Expressions of Emotion in Early Visual Cortex—A Transcranial Magnetic Stimulation Study

Our visual inputs are often entangled with affective meanings in natural vision, implying the existence of extensive interaction between visual and emotional processing. However, little is known about the neural mechanism underlying such interaction. This exploratory transcranial magnetic stimulation (TMS) study examined the possible involvement of the early visual cortex (EVC, Area V1/V2/V3) in perceiving facial expressions of different emotional valences. Across three experiments, single-pulse TMS was delivered at different time windows (50–150 msec) after a brief 10-msec onset of face images, and participants reported the visibility and perceived emotional valence of faces. Interestingly, earlier TMS at ∼90 msec only reduced the face visibility irrespective of displayed expressions, but later TMS at ∼120 msec selectively disrupted the recognition of negative facial expressions, indicating the involvement of EVC in the processing of negative expressions at a later time window, possibly beyond the initial processing of fed-forward facial structure information. The observed TMS effect was further modulated by individuals' anxiety level. TMS at ∼110–120 msec disrupted the recognition of anger significantly more for those scoring relatively low in trait anxiety than the high scorers, suggesting that cognitive bias influences the processing of facial expressions in EVC. Taken together, it seems that EVC is involved in structural encoding of (at least) negative facial emotional valence, such as fear and anger, possibly under modulation from higher cortical areas.

Spatial attention affects the early processing of neutral versus fearful faces when they are task-irrelevant: a classifier study of the EEG C1 component

EEG studies suggest that the emotional content of visual stimuli is processed rapidly. In particular, the C1 component, which occurs up to 100 ms after stimulus onset and likely reflects activity in primary visual cortex V1, has been reported to be sensitive to emotional faces. However, difficulties replicating these results have been reported. We hypothesized that the nature of the task and attentional condition are key to reconcile the conflicting findings. We report three experiments of EEG activity during the C1 time range elicited by peripherally presented neutral and fearful faces under various attentional conditions: the faces were spatially attended or unattended and were either task-relevant or not. Using traditional event-related potential analysis, we found that the early activity changed depending on facial expression, attentional condition, and task. In addition, we trained classifiers to discriminate the different conditions from the EEG signals. Although the classifiers were not able to discriminate between facial expressions in any condition, they uncovered differences between spatially attended and unattended faces but solely when these were task-irrelevant. In addition, this effect was only present for neutral faces. Our study provides further indication that attention and task are key parameters when measuring early differences between emotional and neutral visual stimuli.

General principles of machine learning for brain-computer interfacing

Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.

Exploring the temporal dynamics of speech production with EEG and group ICA

Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have concluded that activity proceeds from posterior to anterior regions of the brain in a sequential manner. Here we tested this claim using the EEG technique. Specifically, participants performed a picture naming task while their naming latencies and scalp potentials were recorded. We performed group temporal Independent Component Analysis (group tICA) to obtain temporally independent component timecourses and their corresponding topographic maps. We identified fifteen components whose estimated neural sources were located in various areas of the brain. The trial-by-trial component timecourses were predictive of the naming latency, implying their involvement in the task. Crucially, we computed the degree of concurrent activity of each component timecourse to test whether activity was sequential or parallel. Our results revealed that these fifteen distinct neural sources exhibit largely concurrent activity during speech production. These results suggest that speech production relies on neural activity that takes place in parallel networks of distributed neural sources.

Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioral responses. Here, we addressed the critical challenge of understanding how aging changes neural information processing mechanisms to delay behavior. Young (20-36 years) and older (60-86 years) adults performed the basic social interaction task of detecting a face versus noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behavior, and also where, when and how EEG activity represents them. We found that occipital-temporal pathway activity dynamically represents the eyes of the face images for behavior ~170 ms poststimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioral deficit of slower reaction times. Our results therefore demonstrate how aging can change neural information processing mechanisms that underlie behavioral slow down.

Active touch facilitates object size perception in children but not adults: A multisensory event related potential study

In order to increase perceptual precision the adult brain dynamically combines redundant information from different senses depending on their reliability. During object size estimation, for example, visual, auditory and haptic information can be integrated to increase the precision of the final size estimate. Young children, however, do not integrate sensory information optimally and instead rely on active touch. Whether this early haptic dominance is reflected in age-related differences in neural mechanisms and whether it is driven by changes in bottom-up perceptual or top-down attentional processes has not yet been investigated. Here, we recorded event-related-potentials from a group of adults and children aged 5-7 years during an object size perception task using auditory, visual and haptic information. Multisensory information was presented either congruently (conveying the same information) or incongruently (conflicting information). No behavioral responses were required from participants. When haptic size information was available via actively tapping the objects, response amplitudes in the mid-parietal area were significantly reduced by information congruency in children but not in adults between 190 ms-250 ms and 310 ms-370 ms. These findings indicate that during object size perception only children's brain activity is modulated by active touch supporting a neural maturational shift from sensory dominance in early childhood to optimal multisensory benefit in adulthood.