Imaging human EEG dynamics using independent component analysis

Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous (taskwork) and collaborative (teamwork) segments of simulation training. The questions posed were: (1) whether these abstract and mostly integrated constructs could be separated neurodynamically; and, (2) what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information (NI), an electroencephalography (EEG) derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence (IBC) which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15-30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters.

Redesigning navigational aids using virtual global landmarks to improve spatial knowledge retrieval

Although beacon- and map-based spatial strategies are the default strategies for navigation activities, today's navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user's sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.

Development of Atypical Reading at Ages 5 to 9 Years and Processing of Speech Envelope Modulations in the Brain

Different studies have suggested that during speech processing readers with dyslexia present atypical levels of neural entrainment as well as atypical functional hemispherical asymmetries in comparison with typical readers. In this study, we evaluated these differences in children and the variation with age before and after starting with formal reading instruction. Synchronized neural auditory processing activity was quantified based on auditory steady-state responses (ASSRs) from EEG recordings. The stimulation was modulated at syllabic and phonemic fluctuation rates present in speech. We measured the brain activation patterns and the hemispherical asymmetries in children at three age points (5, 7, and 9 years old). Despite the well-known heterogeneity during developmental stages, especially in children and in dyslexia, we could extract meaningful common oscillatory patterns. The analyses included (1) the estimations of source localization, (2) hemispherical preferences using a laterality index, measures of neural entrainment, (3) signal-to-noise ratios (SNRs), and (4) connectivity using phase coherence measures. In this longitudinal study, we confirmed that the existence of atypical levels of neural entrainment and connectivity already exists at pre-reading stages. Overall, these measures reflected a lower ability of the dyslectic brain to synchronize with syllabic rate stimulation. In addition, our findings reinforced the hypothesis of a later maturation of the processing of beta rhythms in dyslexia. This investigation emphasizes the importance of longitudinal studies in dyslexia, especially in children, where neural oscillatory patterns as well as differences between typical and atypical developing children can vary in the span of a year.

Dynamics of hidden brain states when people solve verbal puzzles

When people try to solve a problem, they go through distinct steps (encoding, ideation, evaluation, etc.) recurrently and spontaneously. To disentangle different cognitive processes that unfold throughout a trial, we applied an unsupervised machine learning method to electroencephalogram (EEG) data continuously recorded while 39 participants attempted 153 Compound Remote Associates problems (CRA). CRA problems are verbal puzzles that can be solved in either insight-leaning or analysis-leaning manner. We fitted a Hidden Markov Model to the time-frequency transformed EEG signals and decoded each trial as a time-resolved state sequence. The model characterizes hidden brain states with spectrally resolved power topography. Seven states were identified with distinct activation patterns in the theta (4-7 Hz), alpha (8-9 Hz and 10-13 Hz), and gamma (25-50 Hz) bands. Notably, a state featuring widespread activation only in alpha-band frequency emerged, from this data-driven approach, which exhibited dynamic characteristics associated with specific temporal stages and outcomes (whether solved with insight or analysis) of the trials. The state dynamics derived from the model overlap and extend previous literature on the cognitive function of alpha oscillation: the "alpha-state" probability peaks before stimulus onset and decreases before response. In trials solved with insight, relative to solved with analysis, the alpha-state is more likely to be visited and maintained during preparation and solving periods, and its probability declines more sharply immediately preceding a response. This novel paradigm provides a way to extract dynamic features that characterize problem-solving stages and potentially provide a novel window into the nature of the underlying cognitive processes.

Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a technique for studying cortical excitability and connectivity in health and disease, allowing basic research and potential clinical applications. A major methodological issue, severely limiting the applicability of TMS-EEG, relates to the contamination of EEG signals by artifacts of biologic or non-biologic origin. To solve this problem, several methods, based on independent component analysis (ICA), principal component analysis (PCA), signal space projection (SSP), and other approaches, have been developed over the last decade. This article is divided into two parts. In the first part, we review the theoretical background of the currently available TMS-EEG artifact removal methods. In the second part, we formally introduce the mathematics underpinnings of the cleaning methods. We classify them into spatial and temporal filters based on their properties. Since the most frequently used TMS-EEG cleaning approach are spatial filter methods, we focus on them and introduce beamforming as a unified framework of the most popular spatial filtering techniques. This unifying approach enables the comparative assessment of these methods by highlighting their differences in terms of assumptions, challenges, and applicability for different types of artifacts and data. The different properties and challenges of the methods discussed are illustrated with both simulated and recorded data. This article targets non-mathematical and mathematical audiences. Accordingly, those readers interested in essential background information on these methods can focus on Section 2. Whereas theory-oriented readers may find Section 3 helpful for making informed decisions between existing methods and developing the methodology further.

Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.

Estimation of seizure onset zone from ictal scalp EEG using independent component analysis in extratemporal lobe epilepsy

OBJECTIVE

The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of Independent Component Analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected.

APPROACH

We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features.

MAIN RESULTS

For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components.

SIGNIFICANCE

This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.

An EEG-Based Investigation of the Effect of Perceived Observation on Visual Memory in Virtual Environments

The presence of external observers has been shown to affect performance on cognitive tasks, but the parameters of this impact for different types of tasks and the underlying neural dynamics are less understood. The current study examined the behavioral and brain activity effects of perceived observation on participants’ visual working memory (VWM) in a virtual reality (VR) classroom setting, using the task format as a moderating variable. Participants (n = 21) were equipped with a 57-channel EEG cap, and neural data were collected as they completed two VWM tasks under two observation conditions (observed and not observed) in a within-subjects experimental design. The “observation” condition was operationalized through the addition of a static human avatar in the VR classroom. The avatar’s presence was associated with a significant effect on extending the task response time, but no effect was found on task accuracy. This outcome may have been due to a ceiling effect, as the mean participant task scores were quite high. EEG data analysis supported the behavioral findings by showing consistent differences between the no-observation and observation conditions for one of the VWM tasks only. These neural differences were identified in the dorsolateral prefrontal cortex (dlPFC) and the occipital cortex (OC) regions, with higher theta-band activity occurring in the dlPFC during stimulus encoding and in the OC during response selection when the “observing” avatar was present. These findings provide evidence that perceived observation can inhibit performance during visual tasks by altering attentional focus, even in virtual contexts.

Somatosensory perturbations influence cortical activity associated with single-limb balance performance

To determine the association between cortical activity and postural control performance changes with differing somatosensory perturbations. Healthy individuals (n = 15) performed a single-limb balance task under four conditions: baseline, unstable surface (foam), transcutaneous electrical nerve stimulation (TENS) applied to the stance-limb knee, and combined foam + TENS. Cortical activity was recorded with electroencephalography (EEG) and postural sway via triaxial force plate. EEG signals were decomposed, localized, and clustered to generate power spectral density in theta (4-7 Hz) and alpha-2 (10-12 Hz) frequency bands in anatomical clusters. Postural sway signals were analyzed with center of pressure (COP) sway metrics (e.g., area, distance, velocity). Foam increased theta power in the frontal and central clusters (d = 0.77 to 1.16), decreased alpha-2 power in bilateral motor, right parietal, and occipital clusters (d =  - 0.89 to - 2.35) and increased sway area, distance, and velocity (d = 1.09-2.57) relative to baseline. Conversely, TENS decreased central theta power (d =  - 0.60), but increased bilateral motor, left parietal, and occipital alpha-2 power (d = 0.51-1.40), with similar to baseline balance performance. In combination, foam + TENS attenuated sway velocity detriments and cortical activity caused by the foam condition alone. There were weak and moderate associations between percent increased central theta and occipital activity and increased sway velocity. Somatosensory perturbations changed patterns of cortical activity during a single-limb balance task in a manner suggestive of sensory re-weighting to pertinent sensory feedback. Across conditions decreased cortical activity in pre-motor and visual regions were associated with reduced sway velocity.

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals

Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.

Adaptive single-channel EEG artifact removal for real-time clinical monitoring

OBJECTIVE

Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision.

METHODS

We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques.

RESULTS

We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia.

CONCLUSIONS

We show that our method can successfully attenuate various types of artifacts in single-channel EEG.

SIGNIFICANCE

Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

A structured ICA-based process for removing auditory evoked potentials

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (EEG), reflect a combination of TMS-induced cortical activity and multi-sensory responses to TMS. The auditory evoked potential (AEP) is a high-amplitude sensory potential-evoked by the "click" sound produced by every TMS pulse-that can dominate the TEP and obscure observation of other neural components. The AEP is peripherally evoked and therefore should not be stimulation site specific. We address the problem of disentangling the peripherally evoked AEP of the TEP from components evoked by cortical stimulation and ask whether removal of AEP enables more accurate isolation of TEP. We hypothesized that isolation of the AEP using Independent Components Analysis (ICA) would reveal features that are stimulation site specific and unique individual features. In order to improve the effectiveness of ICA for removal of AEP from the TEP, and thus more clearly separate the transcranial-evoked and non-specific TMS-modulated potentials, we merged sham and active TMS datasets representing multiple stimulation conditions, removed the resulting AEP component, and evaluated performance across different sham protocols and clinical populations using reduction in Global and Local Mean Field Power (GMFP/LMFP) and cosine similarity analysis. We show that removing AEPs significantly reduced GMFP and LMFP in the post-stimulation TEP (14 to 400 ms), driven by time windows consistent with the N100 and P200 temporal characteristics of AEPs. Cosine similarity analysis supports that removing AEPs reduces TEP similarity between subjects and reduces TEP similarity between stimulation conditions. Similarity is reduced most in a mid-latency window consistent with the N100 time-course, but nevertheless remains high in this time window. Residual TEP in this window has a time-course and topography unique from AEPs, which follow-up exploratory analyses suggest could be a modulation in the alpha band that is not stimulation site specific but is unique to individual subject. We show, using two datasets and two implementations of sham, evidence in cortical topography, TEP time-course, GMFP/LMFP and cosine similarity analyses that this procedure is effective and conservative in removing the AEP from TEP, and may thus better isolate TMS-evoked activity. We show TEP remaining in early, mid and late latencies. The early response is site and subject specific. Later response may be consistent with TMS-modulated alpha activity that is not site specific but is unique to the individual. TEP remaining after removal of AEP is unique and can provide insight into TMS-evoked potentials and other modulated oscillatory dynamics.

Altered resting-state electroencephalography microstate characteristics in young male smokers

The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients’ impairment.

Multiple Brain Sources Are Differentially Engaged in the Inhibition of Distinct Action Types

Most studies contributing to identify the brain network for inhibitory control have investigated the cancelation of prepared-discrete actions, thus focusing on an isolated and short-lived chunk of human behavior. Aborting ongoing-continuous actions is an equally crucial ability but remains little explored. Although discrete and ongoing-continuous rhythmic actions are associated with partially overlapping yet largely distinct brain activations, it is unknown whether the inhibitory network operates similarly in both situations. Thus, distinguishing between action types constitutes a powerful means to investigate whether inhibition is a generic function. We, therefore, used independent component analysis (ICA) of EEG data and show that canceling a discrete action and aborting a rhythmic action rely on independent brain components. The ICA showed that a delta/theta power increase generically indexed inhibitory activity, whereas N2 and P3 ERP waves did so in an action-specific fashion. The action-specific components were generated by partially distinct brain sources, which indicates that the inhibitory network is engaged differently when canceling a prepared-discrete action versus aborting an ongoing-continuous action. In particular, increased activity was estimated in precentral gyri and posterior parts of the cingulate cortex for action canceling, whereas an enhanced activity was found in more frontal gyri and anterior parts of the cingulate cortex for action aborting. Overall, the present findings support the idea that inhibitory control is differentially implemented according to the type of action to revise.

Atypical processing in neural source analysis of speech envelope modulations in adolescents with dyslexia

Different studies have suggested that language and developmental disorders such as dyslexia are associated with a disturbance of auditory entrainment and of the functional hemispheric asymmetries during speech processing. These disorders typically result from an issue in the phonological component of language that causes problems to represent and manipulate the phonological structure of words at the syllable and/or phoneme level. We used Auditory Steady-State Responses (ASSRs) in EEG recordings to investigate the brain activation and hemisphere asymmetry of theta, alpha, beta and low-gamma range oscillations in typical readers and readers with dyslexia. The aim was to analyse whether the group differences found in previous electrode level studies were caused by a different source activation pattern or conversely was an effect that could be found on the active brain sources. We could not find differences in the brain locations of the main active brain sources. However, we observed differences in the extracted waveforms. The group average of the first DSS component of all signal-to-noise ratios of ASSR at source level was higher than the group averages at the electrode level. These analyses included a lower alpha synchronisation in adolescents with dyslexia and the possibility of compensatory mechanisms in theta, beta and low-gamma frequency bands. The main brain auditory sources were located in cortical regions around the auditory cortex. Thus, the differences observed in auditory EEG experiments would, according to our findings, have their origin in the intrinsic oscillatory mechanisms of the brain cortical sources related to speech perception.

Understanding emerging regulation: The role of frontal electroencephalography asymmetry and negative affectivity

The present study examined frontal electroencephalography (EEG) asymmetry and negative affectivity (NA) as predictors of infant behaviors during the Still-Face Paradigm (SFP). It was hypothesized that infants with lower NA subscale scores who also demonstrate greater left frontal activation would exhibit more frequent social engagement and self-soothing behaviors during the SFP. Mothers reported infant temperament at 6-12 months of age (N = 62), and EEG was recorded during a baseline task and the SFP. Social engagement, distress, and self-soothing behaviors were coded during the SFP. A three-factor solution emerged based on exploratory factor analysis of eight infant behaviors. After considering bivariate relations, multiple regression analyses predicting the behavior factor labeled social engagement (containing vocalizations and handwaving; average factor loading = .56) were conducted separately for asymmetry and NA subscales, controlling for infant sex and age. The SFP asymmetry predicted social engagement after controlling for covariates and baseline asymmetry; however, NA subscales (falling reactivity and distress to limitations) did not uniquely explain significant variance. These findings highlight the importance of frontal EEG asymmetry in contributing to emerging social engagement and regulation in infancy. Implications include potentially utilizing asymmetry markers as screening and intervention targets in the first year of life.

Cortical network and connectivity underlying hedonic olfactory perception

Objective.The emotional response to olfactory stimuli implies the activation of a complex cascade of events triggered by structures lying in the limbic system. However, little is known about how this activation is projected up to cerebral cortex and how different cortical areas dynamically interact each other.Approach.In this study, we acquired EEG from human participants performing a passive odor-perception task with odorants conveying positive, neutral and negative valence. A novel methodological pipeline integrating global field power (GFP), independent component analysis (ICA), dipole source localization was applied to estimate effective connectivity in the challenging scenario of single-trial low-synchronized stimulation.Main results.We identified the brain network and the neural paths, elicited at different frequency bands, i.e.θ(4-7Hz),α(8-12Hz)andβ(13-30Hz), involved in odor valence processing. This brain network includes the orbitofrontal cortex (OFC), the cingulate gyrus (CgG), the superior temporal gyrus (STG), the posterior cingulate cortex/precuneus (PCC/PCu) and the parahippocampal gyrus (PHG). It was analyzed using a time-varying multivariate autoregressive model to resolve time-frequency causal interactions. Specifically, the OFC acts as the main node for odor perception and evaluation of pleasant and unpleasant stimuli, whereas no specific path was observed for a neutral stimulus.Significance.The results introduce new evidences on the role of the OFC during hedonic perception and underpin its specificity during the odor valence assessment. Our findings suggest that, after the odor onset different, bidirectional interactions occur between the OFC and other brain regions associated with emotion recognition/categorization and memory according to the stimulus valence. This outcome unveils how the hedonic olfactory network dynamically changes based on odor valence.

Somatosensory contribution to audio-visual speech processing

Recent studies have demonstrated that the auditory speech perception of a listener can be modulated by somatosensory input applied to the facial skin suggesting that perception is an embodied process. However, speech perception is a multisensory process involving both the auditory and visual modalities. It is unknown whether and to what extent somatosensory stimulation to the facial skin modulates audio-visual speech perception. If speech perception is an embodied process, then somatosensory stimulation applied to the perceiver should influence audio-visual speech processing. Using the McGurk effect (the perceptual illusion that occurs when a sound is paired with the visual representation of a different sound, resulting in the perception of a third sound) we tested the prediction using a simple behavioral paradigm and at the neural level using event-related potentials (ERPs) and their cortical sources. We recorded ERPs from 64 scalp sites in response to congruent and incongruent audio-visual speech randomly presented with and without somatosensory stimulation associated with facial skin deformation. Subjects judged whether the production was /ba/ or not under all stimulus conditions. In the congruent audio-visual condition subjects identifying the sound as /ba/, but not in the incongruent condition consistent with the McGurk effect. Concurrent somatosensory stimulation improved the ability of participants to more correctly identify the production as /ba/ relative to the non-somatosensory condition in both congruent and incongruent conditions. ERP in response to the somatosensory stimulation for the incongruent condition reliably diverged 220 msec after stimulation onset. Cortical sources were estimated around the left anterior temporal gyrus, the right middle temporal gyrus, the right posterior superior temporal lobe and the right occipital region. The results demonstrate a clear multisensory convergence of somatosensory and audio-visual processing in both behavioral and neural processing consistent with the perspective that speech perception is a self-referenced, sensorimotor process.

Inter-brain co-activations during mindfulness meditation. Implications for devotional and clinical settings

Mindfulness meditation usually takes place as personal, introspective activity. It is not known if this practice activates the brain differently when done alone or with someone else. Sixteen couples of expert meditators performed mindfulness-oriented meditation (MOM) and instructed mind-wandering (IMW) tasks in two conditions: once sitting in the same room (SR) and once in two different rooms (DR). Spontaneous electroencephalographic (EEG) data was collected during 7-minute recording sessions in the four experimental settings (MOM/SR, MOM/DR, IMW/SR, IMW/DR). Power in band was computed in separate clusters of independent components of the EEG signals. In addition to significant task effects, found in frontolimbic (MOM > IMW in gamma) and frontoparietal locations (MOM < IMW in theta), significant condition effects were found in frontal (SR > DR in delta) and in temporo-occipital regions (SR > DR in theta and alpha). Moreover, a significant interaction between task and condition revealed higher gamma activity in limbic areas during MOM/SR vs. MOM/DR settings. This effect was not attributable to gender, age nor the meditation expertise of participants. We thus show that the brains of two people work differently when they are doing something together or alone; some of these differences are specific to mindfulness meditation. Implications for devotional and clinical settings are discussed.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

The influence of semantic relevance on the discernment of product appearance and function

Background

This study investigated the impact of semantic relevance on the ability to comprehend the appearance and function of a product, as presented in images.

Methods

The images used the constructs of Simile, Metaphor and Analogy to correspond to congruent, related and incongruent semantic structures, and measured the amplitude of Event-Related Potentials (ERPs) to compare these images with Landscape images. Sixteen participants with design-related educational backgrounds were invited to join in the ERP experiment.

Results

The results found that the image depicting the Metaphor showed a stronger N600 amplitude in the right anterior region of the brain than the Landscape image and the Analogy image induced a stronger N600 effect in the left anterior and right anterior part of the brain than the Landscape image. However, the Simile image did not trigger the N600. The N600 was triggered when the meaning of the Metaphor and Analogy being presented could not be understood. This indicates that a greater processing effort to comprehend them than was required for Simile. Analogy has a wider N600 distribution than Metaphor in the anterior area, suggesting that Analogy would require higher-level thinking processes and more complex semantic processing mechanisms than Metaphor.

Conclusions

The N600 implicated that an assessment method to detect the semantic relationship between appearance and function of a product would assist in determining whether a symbol was suitable to be associated with a product.

Inter trial coherence of low-frequency oscillations in the course of stroke recovery

OBJECTIVE

The aim was to find a sensitive method to highlight the remodeling of the brain's bioelectric activity in post-stroke repair.

METHODS

Fifteen mild upper limb paretic stroke patients and age-matched healthy controls were included. Repeated trials of finger tapping around the 10th and 100th days after stroke onset were recorded with a 128-channel EEG. Power spectra and Inter Trial Coherence (ITC) calculations were synchronized to tappings. ITC was correlated with motor performance.

RESULTS

ITC, in low frequency bands, designates the motor related bioelectric activity in channel space in both healthy subjects and patients. Ten days after stroke onset, delta-theta ITC was severely reduced compared to baseline, while three months later ITC reorganized partially over the ipsilesional central-parietal areas reflecting the improvement of motor networks. Decreased ITC in the central-parietal area remained significant compared to controls. Delta band ITC over the dorsolateral-prefrontal cortex correlates with the performance on Nine Hole Peg Test. At post-recovery, non-paretic hand tappings show significantly decreased delta-theta ITC over the supplementary motor area, which reflects network remodeling.

CONCLUSIONS

Inter Trial Coherence is a useful measure of brain reorganization during stroke recovery.

SIGNIFICANCE

Delta- theta ITC is a sensitive indicator of impaired motor execution.

Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors

Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.

Electrophysiological underpinnings of reward processing: Are we exploiting the full potential of EEG?

Understanding how the brain processes reward is an important and complex endeavor, which has involved the use of a range of complementary neuroimaging tools, including electroencephalography (EEG). EEG has been praised for its high temporal resolution but, because the signal recorded at the scalp is a mixture of brain activities, it is often considered to have poor spatial resolution. Besides, EEG data analysis has most often relied on event-related potentials (ERPs) which cancel out non-phase locked oscillatory activity, thus limiting the functional discriminative power of EEG attainable through spectral analyses. Because these three dimensions -temporal, spatial and spectral- have been unequally leveraged in reward studies, we argue that the full potential of EEG has not been exploited. To back up our claim, we first performed a systematic survey of EEG studies assessing reward processing. Specifically, we report on the nature of the cognitive processes investigated (i.e., reward anticipation or reward outcome processing) and the methods used to collect and process the EEG data (i.e., event-related potential, time-frequency or source analyses). A total of 359 studies involving healthy subjects and the delivery of monetary rewards were surveyed. We show that reward anticipation has been overlooked (88% of studies investigated reward outcome processing, while only 24% investigated reward anticipation), and that time-frequency and source analyses (respectively reported by 19% and 12% of the studies) have not been widely adopted by the field yet, with ERPs still being the dominant methodology (92% of the studies). We argue that this focus on feedback-related ERPs provides a biased perspective on reward processing, by ignoring reward anticipation processes as well as a large part of the information contained in the EEG signal. Finally, we illustrate with selected examples how addressing these issues could benefit the field, relying on approaches combining time-frequency analyses, blind source separation and source localization.

Auditory oddball responses in Tursiops truncatus

Two previous studies suggest that bottlenose dolphins exhibit an "oddball" auditory evoked potential (AEP) to stimulus trains where one of two stimuli has a low probability of occurrence relative to another. However, they reported oddball AEPs at widely different latency ranges (50 vs 500 ms). The present work revisited this experiment in a single dolphin to report the AEPs in response to two tones each assigned probabilities of 0.2, 0.8, and 1 across sessions. The AEP was further isolated from background EEG using independent component analysis, and showed condition effects in the 40-60 ms latency range.

Measuring auditory cortical responses in Tursiops truncatus

Auditory neuroscience in dolphins has largely focused on auditory brainstem responses; however, such measures reveal little about the cognitive processes dolphins employ during echolocation and acoustic communication. The few previous studies of mid- and long-latency auditory-evoked potentials (AEPs) in dolphins report different latencies, polarities, and magnitudes. These inconsistencies may be due to any number of differences in methodology, but these studies do not make it clear which methodological differences may account for the disparities. The present study evaluates how electrode placement and pre-processing methods affect mid- and long-latency AEPs in (Tursiops truncatus). AEPs were measured when reference electrodes were placed on the skin surface over the forehead, the external auditory meatus, or the dorsal surface anterior to the dorsal fin. Data were pre-processed with or without a digital 50-Hz low-pass filter, and the use of independent component analysis to isolate signal components related to neural processes from other signals. Results suggest that a meatus reference electrode provides the highest quality AEP signals for analyses in sensor space, whereas a dorsal reference yielded nominal improvements in component space. These results provide guidance for measuring cortical AEPs in dolphins, supporting future studies of their cognitive auditory processing.

Early Auditory Event-Related Potentials Are Modulated by Alphabetic Literacy Skills in Logographic Chinese Readers

The acquisition of an alphabetic orthography transforms speech processing in the human brain. Behavioral evidence shows that phonological awareness as assessed by meta-phonological tasks like phoneme judgment, is enhanced by alphabetic literacy acquisition. The current study investigates the time-course of the neuro-cognitive operations underlying this enhancement as revealed by event-related potentials (ERPs). Chinese readers with and without proficiency in Jyutping, a Romanization system of Cantonese, were recruited for an auditory onset phoneme judgment task; their behavioral responses and the elicited ERPs were examined. Proficient readers of Jyutping achieved higher response accuracy and exhibited more negative-going ERPs in three early ERP time-windows corresponding to the P1, N1, and P2 components. The phonological mismatch negativity component exhibited sensitivity to both onset and rhyme mismatch in the speech stimuli, but it was not modulated by alphabetic literacy skills. The sustained negativity in the P1-N1-P2 time-windows is interpreted as reflecting enhanced phonetic/phonological processing or attentional/awareness modulation associated with alphabetic literacy and phonological awareness skills.

EEG power spectral dynamics associated with listening in adverse conditions

Adverse listening conditions increase the demand on cognitive resources needed for speech comprehension. In an exploratory study, we aimed to identify independent power spectral features in the EEG useful for studying the cognitive processes involved in this effortful listening. Listeners performed the coordinate response measure task with a single-talker masker at a 0-dB signal-to-noise ratio. Sounds were left unfiltered or degraded with low-pass filtering. Independent component analysis (ICA) was used to identify independent components (ICs) in the EEG data, the power spectral dynamics of which were then analyzed. Frontal midline theta, left frontal, right frontal, left mu, right mu, left temporal, parietal, left occipital, central occipital, and right occipital clusters of ICs were identified. All IC clusters showed some significant listening-related changes in their power spectrum. This included sustained theta enhancements, gamma enhancements, alpha enhancements, alpha suppression, beta enhancements, and mu rhythm suppression. Several of these effects were absent or negligible using traditional channel analyses. Comparison of filtered to unfiltered speech revealed a stronger alpha suppression in the parietal and central occipital clusters of ICs for the filtered speech condition. This not only replicates recent findings showing greater alpha suppression as listening difficulty increases but also suggests that such alpha-band effects can stem from multiple cortical sources. We lay out the advantages of the ICA approach over the restrictive analyses that have been used as of late in the study of listening effort. We also make suggestions for moving into hypothesis-driven studies regarding the power spectral features that were revealed.

Topography of Movement-Related Delta and Theta Brain Oscillations

The aim of this study was to analyse the high density EEG during movement execution guided by visual attention to reveal the detailed topographic distributions of delta and theta oscillations. Twenty right-handed young subjects performed a finger tapping task, paced by a continuously transited repeating visual stimuli. Baseline corrected power of scalp current density transformed EEG was statistically assessed with cluster-based permutation testing. Delta and theta activities revealed differences in their spatial properties at the time of finger tapping execution. Theta synchronization showed a contralateral double activation in the parietal and fronto-central regions, while delta activity appeared in the central contralateral channels. Differences in the spatiotemporal topography between delta and theta activity in the course of movement execution were identified on high density EEG.

EEG hyperscanning in motor rehabilitation: a position paper

Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson's disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.

Modality-specific frequency band activity during neural entrainment to auditory and visual rhythms

Rhythm perception depends on the ability to predict the onset of rhythmic events. Previous studies indicate beta band modulation is involved in predicting the onset of auditory rhythmic events (Fujioka et al., 2009, 2012; Snyder & Large, 2005). We sought to determine if similar processes are recruited for prediction of visual rhythms by investigating whether beta band activity plays a role in a modality-dependent manner for rhythm perception. We looked at electroencephalography time-frequency neural correlates of prediction using an omission paradigm with auditory and visual rhythms. By using omissions, we can separate out predictive timing activity from stimulus-driven activity. We hypothesized that there would be modality-independent markers of rhythm prediction in induced beta band oscillatory activity, and our results support this hypothesis. We find induced and evoked predictive timing in both auditory and visual modalities. Additionally, we performed an exploratory-independent components-based spatial clustering analysis, and describe all resulting clusters. This analysis reveals that there may be overlapping networks of predictive beta activity based on common activation in the parietal and right frontal regions, auditory-specific predictive beta in bilateral sensorimotor regions, and visually specific predictive beta in midline central, and bilateral temporal/parietal regions. This analysis also shows evoked predictive beta activity in the left sensorimotor region specific to auditory rhythms and implicates modality-dependent networks for auditory and visual rhythm perception.

Comparing physiological responses during cognitive tests in virtual environments vs. in identical real-world environments

Immersive virtual environments (VEs) are increasingly used to evaluate human responses to design variables. VEs provide a tremendous capacity to isolate and readily adjust specific features of an architectural or product design. They also allow researchers to safely and effectively measure performance factors and physiological responses. However, the success of this form of design-testing depends on the generalizability of response measurements between VEs and real-world contexts. At the current time, there is very limited research evaluating the consistency of human response data across identical real and virtual environments. Rendering tools were used to precisely replicate a real-world classroom in virtual space. Participants were recruited and asked to complete a series of cognitive tests in the real classroom and in the virtual classroom. Physiological data were collected during these tests, including electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), galvanic skin response (GSR), and head acceleration. Participants' accuracy on the cognitive tests did not significantly differ between the real classroom and the identical VE. However, the participants answered the tests more rapidly in the VE. No significant differences were found in eye blink rate and heart rate between the real and VR settings. Head acceleration and GSR variance were lower in the VE setting. Overall, EEG frequency band-power was not significantly altered between the real-world classroom and the VE. Analysis of EEG event-related potentials likewise indicated strong similarity between the real-world classroom and the VE, with a single exception related to executive functioning in a color-mismatch task.

Recognizing humanity: dehumanization predicts neural mirroring and empathic accuracy in face-to-face interactions

Dehumanization is the failure to recognize the cognitive and emotional complexities of the people around us. While its presence has been well documented in horrific acts of violence, it is also theorized to play a role in everyday life. We measured its presence and effects in face-to-face dyadic interactions between strangers and found that not only was there variance in the extent to which they perceived one another as human, but this variance predicted neural processing and behavior. Specifically, participants showed stronger neural mirroring, indexed by electroencephalography (EEG) mu-suppression, in response to partners they evaluated as more human, suggesting their brains neurally simulated those targets' actions more. Participants were also marginally more empathically accurate about the emotions of partners deemed more human and performed better with them on a cooperative task. These results suggest that there are indeed differences in our recognition of the humanity of people we meet-demonstrated for the first time in a real, face-to-face interaction-and that this mundane variation affects our ability to neurally simulate, cooperate and empathize.

A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG

Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.

The identification of predominant auditory steady-state response brain sources in electroencephalography using denoising source separation

Different approaches have been used to extract auditory steady-state responses (ASSRs) from electroencephalography (EEG) recordings, including region-related electrode configurations (electrode level) and the manual placement of equivalent current dipoles (source level). Inherent limitations of these approaches are the assumption of the anatomical origin and the omission of activity generated by secondary sources. Data-driven methods such as independent component analysis (ICA) seem to avoid these limitations but only to face new others such as the presence of ASSRs with similar properties in different components and the manual selection protocol to select and classify the most relevant components carrying ASSRs. We propose the novel approach of applying a spatial filter to these components in order to extract the most relevant information. We aimed to develop a method based on the reproducibility across trials that performs reliably in low-signal-to-noise ratio (SNR) scenarios using denoising source separation (DSS). DSS combined with ICA successfully reduced the number of components and extracted the most relevant ASSR at 4, 10 and 20 Hz stimulation in group and individual level studies of EEG adolescent data. The anatomical brain location for these low stimulation frequencies showed results in cortical areas with relatively small dispersion. However, for 40 and 80 Hz, results with regard to the number of components and the anatomical origin were less clear. At all stimulation frequencies the outcome measures were consistent with literature, and the partial rejection of inter-subject variability led to more accurate results and higher SNRs. These findings are promising for future applications in group comparison involving pathologies.

Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach

Patients with stroke can experience a drastic change in their body representation (BR), beyond the physical and psychological consequences of stroke itself. Noteworthy, the misperception of BR could affect patients' motor performance even more. Our study aimed at evaluating the usefulness of a robot-aided gait training (RAGT) equipped with augmented visuomotor feedback, expected to target BR (RAGT + VR) in improving lower limb sensorimotor function, gait performance (using Fugl-Meyer Assessment scale for lower extremities, FMA-LE), and BR (using the Body Esteem Scale-BES- and the Body Uneasiness Test-BUT), as compared to RAGT - VR. We also assessed the neurophysiologic basis putatively subtending the BR-based motor function recovery, using EEG recording during RAGT. Forty-five patients with stroke were enrolled in this study and randomized with a 1:2 ratio into either the RAGT + VR (n = 30) or the RAGT - VR (n = 15) group. The former group carried out rehabilitation training with the Lokomat©Pro; whereas, the latter used the Lokomat©Nanos. The rehabilitation protocol consisted of 40 one-hour training sessions. At the end of the training, the RAGT + VR improved in FMA-LE (p < 0.001) and BR (as per BES, (p < 0.001), and BUT, (p < 0.001)) more than the RAGT- did (p < 0.001). These differences in clinical outcomes were paralleled by a greater strengthening of visuomotor connectivity and corticomotor excitability (as detected at the EEG analyses) in the RAGT + VR than in the RAGT - VR (all comparisons p < 0.001), corresponding to an improved motor programming and execution in the former group.We may argue that BR recovery was important concerning functional motor improvement by its integration with the motor control system. This likely occurred through the activation of the Mirror Neuron System secondary to the visuomotor feedback provision, resembling virtual reality. Last, our data further confirm the important role of visuomotor feedback in post-stroke rehabilitation, which can achieve better patient-tailored improvement in functional gait by means of RAGT + VR targeting BR.

Behavioral and ERP Correlates of Long-Term Physical and Mental Training on a Demanding Switch Task

Physical and mental training are associated with positive effects on executive functions throughout the lifespan. However, evidence of the benefits of combined physical and mental regimes over a sedentary lifestyle remain sparse. The goal of this study was to investigate potential mechanisms, from a source-resolved event-related-potential perspective, that could explain how practicing long-term physical and mental exercise can benefit neural processing during the execution of an attention switching task. Fifty-three healthy community volunteers who self-reported long-term practice of Tai Chi (n = 10), meditation + exercise (n = 16), simple aerobics (n = 15), or a sedentary lifestyle (n = 12), aged 47.8 ± 14.6 (SD) were included in this analysis. All participants undertook high-density electroencephalography recording during a switch paradigm. Our results indicate that people who practice physical and mental exercise perform better in a task-switching paradigm. Our analysis revealed an additive effect of the combined practice of physical and mental exercise over physical exercise only. In addition, we confirmed the participation of frontal, parietal and cingulate areas as generators of event-related-potential components (N2-like and P3-like) commonly associated to the performance of switch tasks. Particularly, the N2-like component of the parietal and frontal domains showed significantly greater amplitudes in the exercise and mental training groups compared with aerobics and sedentary groups. Furthermore, we showed better performance associated with greater N2-like amplitudes. Our multivariate analysis revealed that activity type was the most relevant factor to explain the difference between groups, with an important influence of age, and body mass index, and with small effects of educational years, cardiovascular capacity, and sex. These results suggest that chronic combined physical and mental training may confer significant benefits to executive function in normally aging adults, probably through more efficient early attentional processing. Future experimental studies are needed to confirm our results and understand the mechanisms on parieto-frontal networks that contribute to the cognitive improvement associated with practicing combined mental and aerobic exercise, while carefully controlling confounding factors, such as age and body mass index.

Individual versus task differences in slow potential generators

Average slow potentials (SPs) can be computed from any voluntary task, minimally involving attention to anticipated stimuli. Their topography when recorded by large electrode arrays even during simple tasks is complex, multifocal, and its generators appear to be equally multifocal and highly variable across subjects. Various sources of noise of course contaminate such averages and must contribute to the topographic complexity. Here, we report a study in which the non-averaged SP band (0 to 1 Hz) was analyzed by independent components (ICA), from 256 channel recordings of 18 subjects, during four task conditions (resting, visual attention, CPT, and Stroop). We intended to verify whether the replicable SP generators (between two separate day sessions) modeled as current density reconstruction on structural MRI sets were individual-specific, and if putative task-related differences were systematic across subjects. Typically, 3 ICA components (out of 10) explained SPs in each task and subject, and their combined generators were highly variable across subjects: although some occipito-temporal and medial temporal areas contained generators in most subjects; the overall patterns were obviously variable, with no single area common to all 18 subjects. Linear regression modeling to compare combined generators (from all ICA components) between tasks and sessions showed significantly higher correlations between the four tasks than between sessions for each task. Moreover, it was clear that no common task-specific areas could be seen across subjects. Those results represent one more instance in which individual case analyses favor the hypothesis of individual-specific patterns of cortical activity, regardless of task conditions. We discuss this hypothesis with respect to results from the beta band, from individual-case fMRI studies, and its corroboration by functional neurosurgery and the neuropsychology of focal lesions.

Generalized neural decoders for transfer learning across participants and recording modalities

OBJECTIVE

Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.

APPROACH

We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.

MAIN RESULTS

HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features.

SIGNIFICANCE

By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

BACKGROUND

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

METHODS

Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

RESULTS

Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

CONCLUSIONS

Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Cochlear Implant and Hearing Aid: Objective Measures of Binaural Benefit

Cochlear implants (CI) improve hearing for the severely hearing impaired. With an extension of implantation candidacy, today many CI listeners use a hearing aid on their contralateral ear, referred to as bimodal listening. It is uncertain, however, whether the brains of bimodal listeners can combine the electrical and acoustical sound information and how much CI experience is needed to achieve an improved performance with bimodal listening. Patients with bilateral sensorineural hearing loss undergoing implant surgery were tested in their ability to understand speech in quiet and in noise, before and again 3 and 6 months after provision of a CI. Results of these bimodal listeners were compared to age-matched, normal hearing controls (NH). The benefit of adding a contralateral hearing aid was calculated in terms of head shadow, binaural summation, binaural squelch, and spatial release from masking from the results of a sentence recognition test. Beyond that, bimodal benefit was estimated from the difference in amplitudes and latencies of the N1, P2, and N2 potentials of the brains' auditory evoked response (AEP) toward speech. Data of fifteen participants contributed to the results. CI provision resulted in significant improvement of speech recognition with the CI ear, and in taking advantage of the head shadow effect for understanding speech in noise. Some amount of binaural processing was suggested by a positive binaural summation effect 6 month post-implantation that correlated significantly with symmetry of pure tone thresholds. Moreover, a significant negative correlation existed between binaural summation and latency of the P2 potential. With CI experience, morphology of the N1 and P2 potentials in the AEP response approximated that of NH, whereas, N2 remained different. Significant AEP differences between monaural and binaural processing were shown for NH and for bimodal listeners 6 month post-implantation. Although the grand-averaged difference in N1 amplitude between monaural and binaural listening was similar for NH and the bimodal group, source localization showed group-dependent differences in auditory and speech-relevant cortex, suggesting different processing in the bimodal listeners.

Using Posterior EEG Theta Band to Assess the Effects of Architectural Designs on Landmark Recognition in an Urban Setting

The process of urban landmark-based navigation has proven to be difficult to study in a rigorous fashion, primarily due to confounding variables and the problem of obtaining reliable data in real-world contexts. The development of high-resolution, immersive virtual reality technologies has opened exciting new possibilities for gathering data on human wayfinding that could not otherwise be readily obtained. We developed a research platform using a virtual environment and electroencephalography (EEG) to better understand the neural processes associated with landmark usage and recognition during urban navigation tasks. By adjusting the architectural parameters of different buildings in this virtual environment, we isolated and tested specific design features to determine whether or not they served as a target for landmarking. EEG theta band (4-7 Hz) event-related synchronization/desynchronization over posterior scalp areas was evaluated at the time when participants observed each target building along a predetermined self-paced route. A multi-level linear model was used to investigate the effects of salient architectural features on posterior scalp areas. Our results support the conclusion that highly salient architectural features-those that contrast sharply with the surrounding environment-are more likely to attract visual attention, remain in short-term memory, and activate brain regions associated with wayfinding compared with non-salient buildings. After establishing this main aggregate effect, we evaluated specific salient architectural features and neural correlates of navigation processing. The buildings that most strongly associated extended gaze time, location recall accuracy, and changes in theta-band neural patterns with landmarking in our study were those that incorporated rotational twist designs and natural elements such as trees and gardens. Other building features, such as unusual façade patterns or building heights, were to a lesser extent also associated with landmarking.

Target detection in healthy 4-week old piglets from a passive two-tone auditory oddball paradigm

BACKGROUND

Passive auditory oddball tests are effort independent assessments that evaluate auditory processing and are suitable for paediatric patient groups. Our goal was to develop a two-tone auditory oddball test protocol and use this clinical assessment in an immature large animal model. Event-related potentials captured middle latency P1, N1, and P2 responses in 4-week old (N = 16, female) piglets using a custom piglet 32- electrode array on 3 non-consecutive days. The effect of target tone frequency (250 Hz and 4000 Hz) on middle latency responses were tested in a subset of animals.

RESULTS

Results show that infrequent target tone pulses elicit greater N1 amplitudes than frequent standard tone pulses. There was no effect of day. Electrodes covering the front of the head tend to elicit greater waveform responses. P2 amplitudes increased for higher frequency target tones (4000 Hz) than the regular 1000 Hz target tones (p < 0.05).

CONCLUSIONS

Two-tone auditory oddball tests produced consistent responses day-to-day. This clinical assessment was successful in the immature large animal model.

Haptic Perception in Extreme Obesity: qEEG Study Focused on Predictive Coding and Body Schema

Haptic perception (HP) is a perceptual modality requiring manual exploration to elaborate the physical characteristics of external stimuli through multisensory integrative cortical pathways. Cortical areas exploit processes of predictive coding that collect sensorial inputs to build and update internal perceptual models. Modifications to the internal representation of the body have been associated with eating disorders. In the light of this, obese subjects were selected as a valid experimental model to explore predictive coding in haptic perception. To this purpose, we performed electroencephalographic (EEG) continuous recordings during a haptic task in normally weighted versus obese subjects. EEG power spectra were analyzed in different time intervals. The quality of haptic performance in the obese group was poorer than in control subjects, though exploration times were similar. Spectral analysis showed a significant decrease in theta, alpha and beta frequencies in the right temporo-parietal areas of obese group, whereas gamma bands significantly increased in the left frontal areas. These results suggest that severe obesity could be characterized by an impairment in haptic performances and an altered activation of multisensory integrative cortical areas. These are involved in functional coding of external stimuli, which could interfere with the ability to process a predicted condition.

Local and Volume-Conducted Contributions to Cortical Field Potentials

Brain field potentials (FPs) can reach far from their sources, making difficult to know which waves come from where. We show that modern algorithms efficiently segregate the local and remote contributions to cortical FPs by recovering the generator-specific spatial voltage profiles. We investigated experimentally and numerically the local and remote origin of FPs in different cortical areas in anesthetized rats. All cortices examined show significant state, layer, and region dependent contribution of remote activity, while the voltage profiles help identify their subcortical or remote cortical origin. Co-activation of different cortical modules can be discriminated by the distinctive spatial features of the corresponding profiles. All frequency bands contain remote activity, thus influencing the FP time course, in cases drastically. The reach of different FP patterns is boosted by spatial coherence and curved geometry of the sources. For instance, slow cortical oscillations reached the entire brain, while hippocampal theta reached only some portions of the cortex. In anterior cortices, most alpha oscillations have a remote origin, while in the visual cortex the remote theta and gamma even surpass the local contribution. The quantitative approach to local and distant FP contributions helps to refine functional connectivity among cortical regions, and their relation to behavior.

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman-Harris window), EEG time window choices (-750 ms to 0 ms and -250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch's method, Fast Fourier Transform, and Burg's method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

Using Communication to Modulate Neural Synchronization in Teams

Throughout training and team performance, teams may be assessed based on their communication patterns to identify which behaviors contributed to the team’s performance; however, this process of establishing meaning in communication is burdensome and time consuming despite the low monetary cost. A current topic in team research is developing covert measures, which are easier to analyze in real-time, to identify team processes as they occur during team performance; however, little is known about how overt and covert measures of team process relate to one another. In this study, we investigated the relationship between overt (communication) and covert (neural) measures of team process by manipulating the interaction partner (participant or experimenter) team members worked with and the type of task (decision-making or action-based) teams performed to assess their effects on team neural synchronization (measured as neurodynamic entropy) and communication (measured as both flow and content). The results indicated that the type of task affected how the teams structured their communication but had unpredictable effects on the neural synchronization of the team when averaged across the task session. The interaction partner did not affect team neural synchronization when averaged. However, there were significant relationships when communication and neural processes were examined over time between the neurodynamic entropy and the communication flow time series due to both the type of task and the interaction partner. Specifically, significant relationships across time were observed when participants were interacting with the other participant, during the second task trial, and across different regions of the cortex depending on the type of task being performed. The findings from the time series analyses suggest that factors that are previously known to affect communication (interaction partner and task type) also structure the relationship between team communication and neural synchronization—cross-level effects—but only when examined across time. Future research should consider these factors when developing new conceptualizations of team process measurement for measuring team performance over time.