Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments

Paradoxical improvement of cognitive control in older adults under dual-task walking conditions is associated with more flexible reallocation of neural resources: A Mobile Brain-Body Imaging (MoBI) study

Combining walking with a demanding cognitive task is traditionally expected to elicit decrements in gait and/or cognitive task performance. However, it was recently shown that, in a cohort of young adults, most participants improved performance when walking was added to performance of a Go/NoGo response inhibition task. The present study aims to extend these previous findings to an older adult cohort, to investigate whether this improvement when dual-tasking is observed in healthy older adults. Mobile Brain/Body Imaging (MoBI) was used to record electroencephalographic (EEG) activity, three-dimensional (3D) gait kinematics and behavioral responses in the Go/NoGo task, during sitting or walking on a treadmill, in 34 young adults and 37 older adults. Increased response accuracy during walking, independent of age, was found to correlate with slower responses to stimuli (r = 0.44) and with walking-related EEG amplitude modulations over frontocentral regions (r = 0.47) during the sensory gating (N1) and conflict monitoring (N2) stages of inhibition, and over left-lateralized prefrontal regions (r = 0.47) during the stage of inhibitory control implementation. These neural activity changes are related to the cognitive component of inhibition, and they were interpreted as signatures of behavioral improvement during walking. On the other hand, aging, independent of response accuracy during walking, was found to correlate with slower treadmill walking speeds (r = -0.68) and attenuation in walking-related EEG amplitude modulations over left-dominant frontal (r = -0.44) and parietooccipital regions (r = 0.48) during the N2 stage, and over centroparietal regions (r = 0.48) during the P3 stage. These neural activity changes are related to the motor component of inhibition, and they were interpreted as signatures of aging. Older adults whose response accuracy 'paradoxically' improved during walking manifested neural signatures of both behavioral improvement and aging, suggesting that their flexibility in reallocating neural resources while walking might be maintained for the cognitive but not for the motor inhibitory component. These distinct neural signatures of aging and behavior can potentially be used to identify 'super-agers', or individuals at risk for cognitive decline due to aging or neurodegenerative disease.

The order of attentional focus instructions affects how postural control processes compensate for multisensory mismatch: a crossover study

Directing attention during balance training can have an immediate and lasting impact on a patient's balance and ultimately decrease the risk of future falls. However, it is unclear how attention can best be utilized to improve postural control. The current study uses a 2 × 2 crossover design to investigate the potential impact of receiving multiple verbal instructions during a single session of sensorimotor control testing for balance. Twenty-eight healthy adults were tasked to balance on a rocker board while immersed in virtual reality (VR). The VR created a multisensory mismatch between visual VR motion and body motion. The strength of the relationship between visual motion and body motion was measured to assess visual dependence. Alpha and theta frequency bands in electroencephalography (EEG) recordings were also analyzed to identify potential neural correlates of visual dependence and postural stability. Participants were randomized into two groups: one group was first instructed to keep the board leveled (external focus) and then instructed to keep both feet leveled (internal focus) to help maintain stability. The other group was given these two instructions in reverse order. Analyses focused on time, instruction, and group effects from receiving multiple instructions. Results revealed that when participants are given external focus first, and internal focus second, they are more likely to demonstrate lower visual dependence and better postural stability throughout the entire session than participants given internal focus first and external focus second. However, channel-level EEG analyses did not reveal differences between the groups. Current findings suggest that the order of attentional focus instructions may influence how the postural control system resolves sensory incongruence during a single testing session.

Wireless EEG: A survey of systems and studies

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.

Parieto-Occipital Electrocortical Dynamics during Real-World Table Tennis

Traditional human electroencephalography (EEG) experiments that study visuomotor processing use controlled laboratory conditions with limited ecological validity. In the real world, the brain integrates complex, dynamic, multimodal visuomotor cues to guide the execution of movement. The parietal and occipital cortices are especially important in the online control of goal-directed actions. Table tennis is a whole-body, responsive activity requiring rapid visuomotor integration that presents a myriad of unanswered neurocognitive questions about brain function during real-world movement. The aim of this study was to quantify the electrocortical dynamics of the parieto-occipital cortices while playing a sport with high-density electroencephalography. We included analysis of power spectral densities (PSDs), event-related spectral perturbations, intertrial phase coherences (ITPCs), event-related potentials (ERPs), and event-related phase coherences of parieto-occipital source-localized clusters while participants played table tennis with a ball machine and a human. We found significant spectral power fluctuations in the parieto-occipital cortices tied to hit events. Ball machine trials exhibited more fluctuations in θ power around hit events, an increase in intertrial phase coherence and deflection in the event-related potential, and higher event-related phase coherence between parieto-occipital clusters as compared with trials with a human. Our results suggest that sport training with a machine elicits fundamentally different brain dynamics than training with a human.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Using spontaneous eye blink-related brain activity to investigate cognitive load during mobile map-assisted navigation

The continuous assessment of pedestrians' cognitive load during a naturalistic mobile map-assisted navigation task is challenging because of limited experimental control over stimulus presentation, human-map-interactions, and other participant responses. To overcome this challenge, the present study takes advantage of navigators' spontaneous eye blinks during navigation to serve as event markers in continuously recorded electroencephalography (EEG) data to assess cognitive load in a mobile map-assisted navigation task. We examined if and how displaying different numbers of landmarks (3 vs. 5 vs. 7) on mobile maps along a given route would influence navigators' cognitive load during navigation in virtual urban environments. Cognitive load was assessed by the peak amplitudes of the blink-related fronto-central N2 and parieto-occipital P3. Our results show increased parieto-occipital P3 amplitude indicating higher cognitive load in the 7-landmark condition, compared to showing 3 or 5 landmarks. Our prior research already demonstrated that participants acquire more spatial knowledge in the 5- and 7-landmark conditions compared to the 3-landmark condition. Together with the current study, we find that showing 5 landmarks, compared to 3 or 7 landmarks, improved spatial learning without overtaxing cognitive load during navigation in different urban environments. Our findings also indicate a possible cognitive load spillover effect during map-assisted wayfinding whereby cognitive load during map viewing might have affected cognitive load during goal-directed locomotion in the environment or vice versa. Our research demonstrates that users' cognitive load and spatial learning should be considered together when designing the display of future navigation aids and that navigators' eye blinks can serve as useful event makers to parse continuous human brain dynamics reflecting cognitive load in naturalistic settings.

A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism

LAY ABSTRACT

This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.

HArtMuT-modeling eye and muscle contributors in neuroelectric imaging

Objective.Magneto- and electroencephalography (M/EEG) measurements record a mix of signals from the brain, eyes, and muscles. These signals can be disentangled for artifact cleaning e.g. using spatial filtering techniques. However, correctly localizing and identifying these components relies on head models that so far only take brain sources into account.Approach.We thus developed the Head Artifact Model using Tripoles (HArtMuT). This volume conduction head model extends to the neck and includes brain sources as well as sources representing eyes and muscles that can be modeled as single dipoles, symmetrical dipoles, and tripoles. We compared a HArtMuT four-layer boundary element model (BEM) with the EEGLAB standard head model on their localization accuracy and residual variance (RV) using a HArtMuT finite element model (FEM) as ground truth. We also evaluated the RV on real-world data of mobile participants, comparing different HArtMuT BEM types with the EEGLAB standard head model.Main results.We found that HArtMuT improves localization for all sources, especially non-brain, and localization error and RV of non-brain sources were in the same range as those of brain sources. The best results were achieved by using cortical dipoles, muscular tripoles, and ocular symmetric dipoles, but dipolar sources alone can already lead to convincing results.Significance.We conclude that HArtMuT is well suited for modeling eye and muscle contributions to the M/EEG signal. It can be used to localize sources and to identify brain, eye, and muscle components. HArtMuT is freely available and can be integrated into standard software.

Toward Affirmation of Recovery of Deeply Embedded Autobiographical Memory with Background Music and Identification of an EEG Biomarker in Combination with EDA Signal Using Wearable Sensors

There is no disputing the role that background music plays in memory recall. Music has the power to activate the brain and trigger deeply ingrained memories. For dementia patients, background music is a common therapy because of this. Previous studies used music to recall lyrics, series of words, and long- and short-term memories. In this research, electroencephalogram (EEG) and electrodermal activity (EDA) data are collected from 40 healthy participants using wearable sensors during nine music sessions (three happy, three sad, and three neutral). A post-study survey is given to all participants after each piece of music to know if they recalled any autobiographical memories. The main objective is to find an EEG biomarker using the collected qualitative and quantitative data for autobiographical memory recall. The study finds that for all four EEG channels, alpha power rises considerably (on average 16.2%) during the memory “recall” scenario (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0.0288) compared to the “no-recall” situation. Beta power also increased significantly for two channels (F3: p = 0.0100 and F4: p = 0.0210) but not for others (F7: p = 0.6792 and F8: p = 0.0814). Additionally, the phasic standard deviation (p = 0.0260), phasic max (p = 0.0011), phasic energy (p = 0.0478), tonic min (p = 0.0092), tonic standard deviation (p = 0.0171), and phasic energy (p = 0.0478) are significantly different for the EDA signal. The authors conclude by interpreting increased alpha power (8–12 Hz) as a biomarker for autobiographical memory recall.

Event-Related Potential Correlates of Learning to Produce Novel Foreign Phonemes

Learning to pronounce a foreign phoneme requires an individual to acquire a motor program that enables the reproduction of the new acoustic target sound. This process is largely based on the use of auditory feedback to detect pronunciation errors to adjust vocalization. While early auditory evoked neural activity underlies automatic detection and adaptation to vocalization errors, little is known about the neural correlates of acquiring novel speech targets. To investigate the neural processes that mediate the learning of foreign phoneme pronunciation, we recorded event-related potentials when participants (N = 19) pronounced native or foreign phonemes. Behavioral results indicated that the participants' pronunciation of the foreign phoneme improved during the experiment. Early auditory responses (N1 and P2 waves, approximately 85-290 ms after the sound onset) revealed no differences between foreign and native phonemes. In contrast, the amplitude of the frontocentrally distributed late slow wave (LSW, 320-440 ms) was modulated by the pronunciation of the foreign phonemes, and the effect changed during the experiment, paralleling the improvement in pronunciation. These results suggest that the LSW may reflect higher-order monitoring processes that signal successful pronunciation and help learn novel phonemes.

A mobile EEG study on the psychophysiological effects of walking and crowding in indoor and outdoor urban environments

Environmental psychologists have established multiple psychological benefits of interaction with natural, compared to urban, environments on emotion, cognition, and attention. Yet, given the increasing urbanisation worldwide, it is equally important to understand how differences within different urban environments influence human psychological experience. We developed a laboratory experiment to examine the psychophysiological effects of the physical (outdoor or indoor) and social (crowded versus uncrowded) environment in healthy young adults, and to validate the use of mobile electroencephalography (EEG) and electrodermal activity (EDA) measurements during active walking. Participants (N = 42) were randomly assigned into a walking or a standing group, and watched six 1-min walk-through videos of green, urban indoor and urban outdoor environments, depicting high or low levels of social density. Self-reported emotional states show that green spaces is perceived as more calm and positive, and reduce attentional demands. Further, the outdoor urban space is perceived more positively than the indoor environment. These findings are consistent with earlier studies on the psychological benefits of nature and confirm the effectiveness of our paradigm and stimuli. In addition, we hypothesised that even short-term exposure to crowded scenes would have negative psychological effects. We found that crowded scenes evoked higher self-reported arousal, more negative self-reported valence, and recruited more cognitive and attentional resources. However, in walking participants, they evoked higher frontal alpha asymmetry, suggesting more positive affective responses. Furthermore, we found that using recent signal-processing methods, the EEG data produced a comparable signal-to-noise ratio between walking and standing, and that despite differences between walking and standing, skin-conductance also captured effectively psychophysiological responses to stimuli. These results suggest that emotional responses to visually presented stimuli can be measured effectively using mobile EEG and EDA in ambulatory settings, and that there is complex interaction between active walking, the social density of urban spaces, and direct and indirect affective responses to such environments.

Processing of task-irrelevant sounds during typical everyday activities in children

Our ability to focus on a task and ignore task-irrelevant stimuli is critical for efficient cognitive functioning. Attention control is especially required in the auditory modality as sound has privileged access to perception and consciousness. Despite this important function, little is known about auditory attention during typical everyday activities in childhood. We investigated the impact of task-irrelevant sounds on attention during three everyday activities - playing a game, reading a book, watching a movie. During these activities, environmental novel sounds were presented within a sequence of standard sounds to 7-8-year-old children and adults. We measured ERPs reflecting early sound processing and attentional orienting and theta power evoked by standard and novel sounds during these activities. Playing a game versus reading or watching reduced early encoding of sounds in children and affected ongoing information processing and attention allocation in both groups. In adults, theta power was reduced during playing at mid-central brain areas. Results show a pattern of immature neuronal mechanisms underlying perception and attention of task-irrelevant sounds in 7-8-year-old children. While the type of activity affected the processing of irrelevant sounds in both groups, early stimulus encoding processes were more sensitive to the type of activities in children.

Intention-based predictive information modulates auditory deviance processing

The human brain is highly responsive to (deviant) sounds violating an auditory regularity. Respective brain responses are usually investigated in situations when the sounds were produced by the experimenter. Acknowledging that humans also actively produce sounds, the present event-related potential study tested for differences in the brain responses to deviants that were produced by the listeners by pressing one of two buttons. In one condition, deviants were unpredictable with respect to the button-sound association. In another condition, deviants were predictable with high validity yielding correctly predicted deviants and incorrectly predicted (mispredicted) deviants. Temporal principal component analysis revealed deviant-specific N1 enhancement, mismatch negativity (MMN) and P3a. N1 enhancements were highly similar for each deviant type, indicating that the underlying neural mechanism is not affected by intention-based expectation about the self-produced forthcoming sound. The MMN was abolished for predictable deviants, suggesting that the intention-based prediction for a deviant can overwrite the prediction derived from the auditory regularity (predicting a standard). The P3a was present for each deviant type but was largest for mispredicted deviants. It is argued that the processes underlying P3a not only evaluate the deviant with respect to the fact that it violates an auditory regularity but also with respect to the intended sensorial effect of an action. Overall, our results specify current theories of auditory predictive processing, as they reveal that intention-based predictions exert different effects on different deviance-specific brain responses.

Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis

Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.

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.

The impact of external and internal focus of attention on visual dependence and EEG alpha oscillations during postural control

Background

The ability to maintain upright posture requires successful integration of multiple sensory inputs (visual, vestibular, and somatosensory). When one or more sensory systems become unreliable, the postural control system must “down-weight” (or reduce the influence of) those senses and rely on other senses to maintain postural stability. As individuals age, their ability to successfully reweight sensory inputs diminishes, leading to increased fall risk. The present study investigates whether manipulating attentional focus can improve the ability to prioritize different sensory inputs for postural control.

Methods

Forty-two healthy adults stood on a balance board while wearing a virtual reality (VR) head-mounted display. The VR environment created a multisensory conflict amongst the different sensory signals as participants were tasked with maintaining postural stability on the balance board. Postural sway and scalp electroencephalography (EEG) were measured to assess visual weighting and cortical activity changes. Participants were randomized into groups that received different instructions on where to focus their attention during the balance task.

Results

Following the instructions to direct attention toward the movement of the board (external focus group) was associated with lower visual weighting and better balance performance than when not given any instructions on attentional focus (control group). Following the instructions to direct attention towards movement of the feet (internal focus group) did not lead to any changes in visual weighting or balance performance. Both external and internal focus groups exhibited increased EEG alpha power (8–13 Hz) activity over the occipital cortex as compared to the control group.

Conclusions

Current results suggest that directing one’s attention externally, away from one’s body, may optimize sensory integration for postural control when visual inputs are incongruent with somatosensory and vestibular inputs. Current findings may be helpful for clinicians and researchers in developing strategies to improve sensorimotor mechanisms for balance.

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Understanding learners' emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

Neuroscience and architecture: Modulating behavior through sensorimotor responses to the built environment

As we move through the world, natural and built environments implicitly guide behavior by appealing to certain sensory and motor dynamics. This process can be motivated by automatic attention to environmental features that resonate with specific sensorimotor responses. This review aims at providing a psychobiological framework describing how environmental features can lead to automated sensorimotor responses through defined neurophysiological mechanisms underlying attention. Through the use of automated processes in subsets of cortical structures, the goal of this framework is to describe on a neuronal level the functional link between the designed environment and sensorimotor responses. By distinguishing between environmental features and sensorimotor responses we elaborate on how automatic behavior employs the environment for sensorimotor adaptation. This is realized through a thalamo-cortical network integrating environmental features with motor aspects of behavior. We highlight the underlying transthalamic transmission from an Enactive and predictive perspective and review recent studies that effectively modulated behavior by systematically manipulating environmental features. We end by suggesting a promising combination of neuroimaging and computational analysis for future studies.

Emotion Regulation Choice in Internet Addiction: Less Reappraisal, Lower Frontal Alpha Asymmetry

Individuals with internet addiction (IA) show difficulties in emotion regulation. However, they could effectively employ emotion regulation strategies when instructed. We speculate that this discrepancy might be caused by maladaptive emotion regulation choices. Recent studies indicated that decreased activity of the left frontal cortex could be a neural marker of reappraisal use. To address this problem, individuals with IA (n = 17, IA group) and healthy individuals (n = 23, healthy control [HC] group) were required to choose an emotion regulation strategy between reappraisal and distraction to regulate their emotions varying in emotional intensity and valence. We also compared the resting state frontal alpha asymmetry (FAA) of these 2 groups. The results replicated more choices of reappraisal in low- versus high-intensity emotional contexts across groups. More importantly, the IA group chose reappraisal less frequently compared with the HC group, irrespective of emotional intensity. Furthermore, we found individuals with IA have lower FAA than healthy controls, and FAA shows a positive correlation with the use of reappraisal. These findings suggest that IA alters individuals' patterns of emotion regulation choice and impairs frontal activities, causing difficulties in emotion regulation.

The 4E approach to the human microbiome: Nested interactions between the gut-brain/body system within natural and built environments

The complexity of the human mind and its interaction with the environment is one of the main epistemological debates throughout history. Recent ideas, framed as the 4E perspective to cognition, highlight that human experience depends causally on both cerebral and extracranial processes, but also is embedded in a particular sociomaterial context and is a product of historical accumulation of trajectory changes throughout life. Accordingly, the human microbiome is one of the most intriguing actors modulating brain function and physiology. Here, we present the 4E approach to the Human Microbiome for understanding mental processes from a broader perspective, encompassing one's body physiology and environment throughout their lifespan, interconnected by microbiome community structure and dynamics. We review evidence supporting the approach theoretically and motivates the study of the global set of microbial ecosystem networks encountered by a person across their lifetime (from skin to gut to natural and built environments). We furthermore trace future empirical implementation of the approach. We finally discuss novel research opportunities and clinical interventions aimed toward developing low-cost/high-benefit integrative and personalized bio-psycho-socio-environmental treatments for mental health and including the brain-gut-microbiome axis.

Zapline‐plus: A Zapline extension for automatic and adaptive removal of frequency‐specific noise artifacts in M/ EEG

Removing power line noise and other frequency‐specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline‐plus, which allows adaptive and automatic removal of frequency‐specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to‐be‐removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re‐running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise.

Development of an EEG Headband for Stress Measurement on Driving Simulators

In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.

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.

Towards real-world neuroscience using mobile EEG and augmented reality

Our visual environment impacts multiple aspects of cognition including perception, attention and memory, yet most studies traditionally remove or control the external environment. As a result, we have a limited understanding of neurocognitive processes beyond the controlled lab environment. Here, we aim to study neural processes in real-world environments, while also maintaining a degree of control over perception. To achieve this, we combined mobile EEG (mEEG) and augmented reality (AR), which allows us to place virtual objects into the real world. We validated this AR and mEEG approach using a well-characterised cognitive response-the face inversion effect. Participants viewed upright and inverted faces in three EEG tasks (1) a lab-based computer task, (2) walking through an indoor environment while seeing face photographs, and (3) walking through an indoor environment while seeing virtual faces. We find greater low frequency EEG activity for inverted compared to upright faces in all experimental tasks, demonstrating that cognitively relevant signals can be extracted from mEEG and AR paradigms. This was established in both an epoch-based analysis aligned to face events, and a GLM-based approach that incorporates continuous EEG signals and face perception states. Together, this research helps pave the way to exploring neurocognitive processes in real-world environments while maintaining experimental control using AR.

Neural Dynamics of Context-sensitive Adjustments in Cognitive Flexibility

To adaptively interact with the uncertainties of daily life, we must match our level of cognitive flexibility to situations that place different demands on our ability to focus on the current task while remaining sensitive to cues that signal other, more urgent tasks. Such cognitive-flexibility adjustments in response to changing contextual demands (metaflexibility) have been observed in cued task-switching paradigms, where the performance cost incurred by switching versus repeating tasks (switch cost) scales inversely with the proportion of switches (PS) within a block of trials. However, the neural underpinnings of these adjustments in cognitive flexibility are not well understood. Here, we recorded 64-channel EEG measures of electrical brain activity as participants switched between letter and digit categorization tasks in varying PS contexts, from which we extracted ERPs elicited by the task cue and EEG alpha-power differences during both the cue-to-target interval and the resting precue period. The temporal resolution of EEG/ERPs allowed us to test whether contextual adjustments in cognitive flexibility are mediated by tonic changes in processing mode, or by changes in phasic, task-cue-triggered processes. We observed reliable modulation of behavioral switch cost by PS context that were mirrored in both cue-evoked ERP and time-frequency effects, but not in blockwide precue EEG changes. These results indicate that different levels of cognitive flexibility are instantiated in response to the presentation of task cues, rather than by being maintained as a tonic neural-activity state difference between low- and high-switch contexts.

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.

Time to move: Brain dynamics underlying natural action and cognition

Advances in Mobile Brain/Body Imaging (MoBI) technology allows for real-time measurements of human brain dynamics during every day, natural, real-life situations. This special issue Time to Move brings together a collection of experimental papers, targeted reviews and opinion articles that lay out the latest MoBI findings. A wide range of topics across different fields are covered including art, athletics, virtual reality, and mobility. What unites these diverse topics is the common goal to enhance and restore human abilities by reaching a better understanding on how cognition is implemented by the brain-body relationship. The breadth and novelty of paradigms and findings reported here positions MoBI as a new frontier in the field of human cognitive neuroscience.

Human cortical dynamics during full-body heading changes

The retrosplenial complex (RSC) plays a crucial role in spatial orientation by computing heading direction and translating between distinct spatial reference frames based on multi-sensory information. While invasive studies allow investigating heading computation in moving animals, established non-invasive analyses of human brain dynamics are restricted to stationary setups. To investigate the role of the RSC in heading computation of actively moving humans, we used a Mobile Brain/Body Imaging approach synchronizing electroencephalography with motion capture and virtual reality. Data from physically rotating participants were contrasted with rotations based only on visual flow. During physical rotation, varying rotation velocities were accompanied by pronounced wide frequency band synchronization in RSC, the parietal and occipital cortices. In contrast, the visual flow rotation condition was associated with pronounced alpha band desynchronization, replicating previous findings in desktop navigation studies, and notably absent during physical rotation. These results suggest an involvement of the human RSC in heading computation based on visual, vestibular, and proprioceptive input and implicate revisiting traditional findings of alpha desynchronization in areas of the navigation network during spatial orientation in movement-restricted participants.

Decoding Covert Speech From EEG-A Comprehensive Review

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

The brain dynamics of architectural affordances during transition

Action is a medium of collecting sensory information about the environment, which in turn is shaped by architectural affordances. Affordances characterize the fit between the physical structure of the body and capacities for movement and interaction with the environment, thus relying on sensorimotor processes associated with exploring the surroundings. Central to sensorimotor brain dynamics, the attentional mechanisms directing the gating function of sensory signals share neuronal resources with motor-related processes necessary to inferring the external causes of sensory signals. Such a predictive coding approach suggests that sensorimotor dynamics are sensitive to architectural affordances that support or suppress specific kinds of actions for an individual. However, how architectural affordances relate to the attentional mechanisms underlying the gating function for sensory signals remains unknown. Here we demonstrate that event-related desynchronization of alpha-band oscillations in parieto-occipital and medio-temporal regions covary with the architectural affordances. Source-level time-frequency analysis of data recorded in a motor-priming Mobile Brain/Body Imaging experiment revealed strong event-related desynchronization of the alpha band to originate from the posterior cingulate complex, the parahippocampal region as well as the occipital cortex. Our results firstly contribute to the understanding of how the brain resolves architectural affordances relevant to behaviour. Second, our results indicate that the alpha-band originating from the occipital cortex and parahippocampal region covaries with the architectural affordances before participants interact with the environment, whereas during the interaction, the posterior cingulate cortex and motor areas dynamically reflect the affordable behaviour. We conclude that the sensorimotor dynamics reflect behaviour-relevant features in the designed environment.

Eye movement-related brain potentials during assisted navigation in real-world environments

Conducting neuroscience research in the real-world remains challenging because of movement- and environment-related artifacts as well as missing control over stimulus presentation. The present study overcame these restrictions by mobile electroencephalography (EEG) and data-driven analysis approaches during a real-world navigation task. During assisted navigation through an unfamiliar city environment, participants received either standard or landmark-based auditory navigation instructions. EEG data were recorded continuously during navigation. Saccade- and blink-events as well as gait-related EEG activity were extracted from sensor level data. Brain activity associated with the navigation task was identified by subsequent source-based cleaning of non-brain activity and unfolding of overlapping event-related potentials. When navigators received landmark-based instructions compared to those receiving standard navigation instructions, the blink-related brain potentials during navigation revealed higher amplitudes at fronto-central leads in a time window starting at 300 ms after blinks, which was accompanied by improved spatial knowledge acquisition tested in follow-up spatial tasks. Replicating improved spatial knowledge acquisition from previous experiments, the present study revealed eye movement-related brain potentials to point to the involvement of higher cognitive processes and increased processing of incoming information during periods of landmark-based instructions. The study revealed neuronal correlates underlying visuospatial information processing during assisted navigation in the real-world providing a new analysis approach for neuroscientific research in freely moving participants in uncontrollable real-world environments.