Neuromotor changes in postural control following bed rest

Chronic bed rest (BR) serves as a model for studying the effects of prolonged immobility on physiological and neuromotor functions, particularly postural control. Prolonged BR leads to significant deconditioning of postural balance control, characterized by increased sway path lengths, sway velocity and fall risk, independent of muscle strength. These changes are linked to neural adaptations at spinal and supraspinal levels, including structural and functional brain changes, such as alterations in grey and white matter, increased cerebellar activation, reduced spinal excitability and increased latencies within reflex circuitries. Additionally, BR disrupts sensory integration from proprioceptive, visual and vestibular systems, impairing postural stability. Visual reliance remains stable during BR, though decreased visual acuity and contrast sensitivity are noted. Moreover, BR-induced shifts in cerebrospinal fluid contribute to altered brain activity, impacting sensorimotor function. Vestibular system adaptations, including changes in vestibulospinal reflexes, further exacerbate balance impairments. Understanding these mechanisms is crucial for developing interventions to mitigate the adverse effects of BR on postural control and prevent prolonged recovery times or increased risk of injury. This review highlights the need for further research into the neural underpinnings of BR-induced postural instability, with a focus on sensory integration and neuroplasticity.

The Feasibility and Test-Retest Reliability of Wireless Dry-Electrode EEG During a Dynamic Psychomotor Virtual Reality Task

PURPOSE

Virtual reality (VR) offers immersive environments for studying psychomotor performance, but the reliability of dry-electrode electroencephalography (EEG) in assessing brain activity during dynamic VR exergames (VRex) remains unclear. The present study investigated the feasibility and reliability of dry-electrode EEG frequency band, with primary focus on alpha band activity.

METHODS

Ten amateur combat sports male participants (37 ± 11 years) volunteered for this study. The feasibility of dry-electrode EEG recording during motion and test-retest (24 h) reliability, was investigated. EEG measurements were obtained pre, post, and throughout a standardized boxing focus ball VRex session, comprising three 3-min rounds interspersed with 1-min rest intervals. EEG data were analyzed globally and at each electrode site, calculating average power spectral density values.

FINDINGS

ICCs data indicated poor-to-excellent (0.208-0.858) reliability across all measurements within the 4- to 30-Hz frequency range. Poor-to-good reliability (0.393-0.636) was found across the task-active VRex intervals. Electrode sites ranged in reliability from poor (electrode P3; 0.262) to excellent (electrode P4; 0.728), with higher reliability found in the alpha band across electrode sites compared to average spectral band values.

CONCLUSION

The present study demonstrates the feasibility, although variable reliability, in neuronal detection during a dynamic VR task, using novel dry-electrode EEG technology.

Approach-Avoidance Bias in Virtual and Real-World Simulations: Insights from a Systematic Review of Experimental Setups

BACKGROUND

Approach and avoidance bias (AAB) describes automatic behavioral tendencies to react toward environmental stimuli regarding their emotional valence. Traditional setups have provided evidence but often lack ecological validity. The study of the AAB in naturalistic contexts has recently increased, revealing significant methodological challenges. This systematic review evaluates the use of virtual reality (VR) and real-world setups to study the AAB, summarizing methodological innovations and challenges.

METHODS

We systematically reviewed peer-reviewed articles employing VR and real-world setups to investigate the AAB. We analyzed experimental designs, stimuli, response metrics, and technical aspects to assess their alignment with research objectives and identify limitations.

RESULTS

This review included 14 studies revealing diverse methodologies, stimulus types, and novel behavioral responses, highlighting significant variability in design strategies and methodological coherence. Several studies used traditional reaction time measures yet varied in their application of VR technology and participant interaction paradigms. Some studies showed discrepancies between simulated and natural bodily actions, while others showcased more integrated approaches that preserved their integrity. Only a minority of studies included control conditions or acquired (neuro)physiological data.

CONCLUSIONS

VR offers a potential ecological setup for studying the AAB, enabling dynamic and immersive interactions. Our results underscore the importance of establishing a coherent framework for investigating the AAB tendencies using VR. Addressing the foundational challenges of developing baseline principles that guide VR-based designs to study the AAB within naturalistic contexts is essential for advancing the AAB research and application. This will ultimately contribute to more reliable and reproducible experimental paradigms and develop effective interventions that help individuals recognize and change their biases, fostering more balanced behaviors.

Exploring Relevant Features for EEG-Based Investigation of Sound Perception in Naturalistic Soundscapes

A comprehensive analysis of everyday sound perception can be achieved using electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram. Using continuous data analysis, we contrast different features in terms of their predictive power for unseen data and thus their distinct contributions to explaining neural data. For this, we analyze data from a complex audio-visual motor task using a naturalistic soundscape. The results demonstrated that the feature sets that explain the most neural variability were a combination of highly detailed acoustic features with a comprehensive description of specific sound onsets. Furthermore, it showed that established features can be applied to complex soundscapes. Crucially, the outcome hinged on excluding periods devoid of sound onsets in the analysis in the case of the discrete features. Our study highlights the importance to comprehensively describe the soundscape, using acoustic and non-acoustic aspects, to fully understand the dynamics of sound perception in complex situations. This approach can serve as a foundation for future studies aiming to investigate sound perception in natural settings.

The speed and phase of locomotion dictate saccade probability and simultaneous low-frequency power spectra

Every day we make thousands of saccades and take thousands of steps as we explore our environment. Despite their common co-occurrence in a typical active state, we know little about the coordination between eye movements, walking behaviour and related changes in cortical activity. Technical limitations have been a major impediment, which we overcome here by leveraging the advantages of an immersive wireless virtual reality (VR) environment with three-dimensional (3D) position tracking, together with simultaneous recording of eye movements and mobile electroencephalography (EEG). Using this approach with participants engaged in unencumbered walking along a clear, level path, we find that the likelihood of eye movements at both slow and natural walking speeds entrains to the rhythm of footfall, peaking after the heel-strike of each step. Compared to previous research, this entrainment was captured in a task that did not require visually guided stepping - suggesting a persistent interaction between locomotor and visuomotor functions. Simultaneous EEG recordings reveal a concomitant modulation entrained to heel-strike, with increases and decreases in oscillatory power for a broad range of frequencies. The peak of these effects occurred in the theta and alpha range for slow and natural walking speeds, respectively. Together, our data show that the phase of the step-cycle influences other behaviours such as eye movements, and produces related modulations of simultaneous EEG following the same rhythmic pattern. These results reveal gait as an important factor to be considered when interpreting saccadic and time-frequency EEG data in active observers, and demonstrate that saccadic entrainment to gait may persist throughout everyday activities.

Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots

Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.

The moving wave: Applications of the mobile EEG approach to study human attention

Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.

Mobile EEG for the study of cognitive-motor interference during swimming?

Research on brain function in natural environments has become a new interest in cognitive science. In this study, we aim to advance mobile electroencephalography (EEG) participant and device mobility. We investigated the feasibility of measuring human brain activity using mobile EEG during a full-body motion task as swimming, by the example of cognitive-motor interference (CMI). Eleven participants were given an auditory oddball task while sitting and swimming, with mobile EEG recording ongoing brain activity. Measures of interest were event-related potentials (ERPs) elicited by experimental stimuli. While the auditory N100 was measured to verify signal quality, the P300 to task-relevant stimuli served as a marker of CMI effects. Analyzes were first performed within subjects, while binomial tests assessed the proportion of significant effects. Event-related changes in the time-frequency domain around turns during swimming were analyzed in an exploratory fashion. The successful recording of the N100 in all conditions shows that the setup was functional throughout the experiment. Regarding CMI, we did not find reliable changes in P300 amplitude in different motor settings in all subjects. However, we found plausible modulations in the alpha/mu and beta bands before and after turns. This study shows that it is generally feasible to measure mobile EEG in the time and time-frequency domain in an aquatic environment while subjects are freely moving. We see promising potential in the use of mobile EEG in extreme settings, advancing toward the application of mobile EEG in more real-life situations.

A systematic review of mobile brain/body imaging studies using the P300 event-related potentials to investigate cognition beyond the laboratory

The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments

Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.

Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration

Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait

Walking is a complex motor activity that requires coordinated interactions between the sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young people, older people, and individuals with Parkinson's disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralized network activated during the left swing phase, and a right-lateralized network active during the right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. Individuals with the impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interindividual differences in neuromuscular connectivity during gait.

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

Reduced Proactive and Reactive Cognitive Flexibility in Older Adults Underlies Performance Costs During Dual-Task Walking: A Mobile Brain/Body Imaging (MoBI) Study

Age-related reductions in cognitive flexibility may limit modulation of control processes during systematic increases to cognitive-motor demands, exacerbating dual-task costs. In this study, behavioral and neurophysiologic changes to proactive and reactive control during progressive cognitive-motor demands were compared across older and younger adults to explore the basis for age-differences in cognitive-motor interference (CMI). 19 younger (19 - 29 years old, mean age = 22.84 +/- 2.75 years, 6 male, 13 female) and 18 older (60 - 77 years old, mean age = 67.89 +/- 4.60 years, 9 male, 9 female) healthy adults completed cued task-switching while alternating between sitting and walking on a treadmill. Gait kinematics, task performance measures, and brain activity were recorded using electroencephalography (EEG) based Mobile Brain/Body Imaging (MoBI). Response accuracy on easier trial types improved in younger, but not older adults when they walked while performing the cognitive task. As difficulty increased, walking provoked accuracy costs in older, but not younger adults. Both groups registered faster responses and reduced gait variability during dual-task walking. Older adults exhibited lower amplitude modulations of proactive and reactive neural activity as cognitive-motor demands systematically increased, which may reflect reduced flexibility for progressive preparatory and reactive adjustments over behavioral control.

Neuromuscular assessment of force development, postural, and gait performance under cognitive-motor dual-tasking in healthy older adults and people with early Parkinson's disease: Study protocol for a cross-sectional Mobile Brain/Body Imaging (MoBI) study

Background

Neuromuscular dysfunction is common in older adults and more pronounced in neurodegenerative diseases. In Parkinson's disease (PD), a complex set of factors often prevents the effective performance of activities of daily living that require intact and simultaneous performance of the motor and cognitive tasks.

Methods

The cross-sectional study includes a multifactorial mixed-measure design. Between-subject factor grouping the sample will be Parkinson's Disease (early PD vs. healthy). The within-subject factors will be the task complexity (single- vs. dual-task) in each motor activity, i.e., overground walking, semi-tandem stance, and isometric knee extension, and a walking condition (wide vs. narrow lane) will be implemented for the overground walking activity only. To study dual-task (DT) effects, in each motor activity participants will be given a secondary cognitive task, i.e., a visual discrimination task for the overground walking, an attention task for the semi-tandem, and mental arithmetic for the isometric extension. Analyses of DT effects and underlying neuronal correlates will focus on both gait and cognitive performance where applicable. Based on an a priori sample size calculation, a total N = 42 older adults (55-75 years) will be recruited. Disease-specific changes such as laterality in motor unit behavior and cortical control of movement will be studied with high-density surface electromyography and electroencephalography during static and dynamic motor activities, together with whole-body kinematics.

Discussion

This study will be one of the first to holistically address early PD neurophysiological and neuromuscular patterns in an ecologically valid environment under cognitive-motor DT conditions of different complexities. The outcomes of the study aim to identify the biomarker for early PD either at the electrophysiological, muscular or kinematic level or in the communication between these systems.

Clinical Trial Registration

ClinicalTrials.Gov, NCT05477654. This study was approved by the Medical Ethical Committee (106/2021).

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Moving beyond the lab: investigating empathy through the Empirical 5E approach

Empathy is a complex and multifaceted phenomenon that plays a crucial role in human social interactions. Recent developments in social neuroscience have provided valuable insights into the neural underpinnings and bodily mechanisms underlying empathy. This methodology often prioritizes precision, replicability, internal validity, and confound control. However, fully understanding the complexity of empathy seems unattainable by solely relying on artificial and controlled laboratory settings, while overlooking a comprehensive view of empathy through an ecological experimental approach. In this article, we propose articulating an integrative theoretical and methodological framework based on the 5E approach (the "E"s stand for embodied, embedded, enacted, emotional, and extended perspectives of empathy), highlighting the relevance of studying empathy as an active interaction between embodied agents, embedded in a shared real-world environment. In addition, we illustrate how a novel multimodal approach including mobile brain and body imaging (MoBi) combined with phenomenological methods, and the implementation of interactive paradigms in a natural context, are adequate procedures to study empathy from the 5E approach. In doing so, we present the Empirical 5E approach (E5E) as an integrative scientific framework to bridge brain/body and phenomenological attributes in an interbody interactive setting. Progressing toward an E5E approach can be crucial to understanding empathy in accordance with the complexity of how it is experienced in the real world.

The brain in motion-cognitive effects of simultaneous motor activity

During the last 30 years, a large number of behavioral studies have investigated the effect of simultaneous exercise on cognitive functions. The heterogeneity of the results has been attributed to different parameters, such as intensity or modality of physical activity, and the investigated cognitive processes. More recent methodological improvements have enabled to record electroencephalography (EEG) during physical exercise. EEG studies combining cognitive tasks with exercise have described predominantly detrimental effects on cognitive processes and EEG parameters. However, differences in the underlying rationale and the design of EEG versus behavioral studies make direct comparisons between both types of studies difficult. In this narrative review of dual-task experiments we evaluated behavioral and EEG studies and discuss possible explanations for the heterogeneity of results and for the discrepancy between behavioral and EEG studies. Furthermore, we provide a proposal for future EEG studies on simultaneous motion to be a useful complement to behavioral studies. A crucial factor might be to find for each cognitive function the motor activity that matches this function in terms of attentional focus. This hypothesis should be investigated systematically in future studies.

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.

Stroop in motion: Neurodynamic modulation underlying interference control while sitting, standing, and walking

There is conflicting evidence about how interference control in healthy adults is affected by walking as compared to standing or sitting. Although the Stroop paradigm is one of the best-studied paradigms to investigate interference control, the neurodynamics associated with the Stroop task during walking have never been studied. We investigated three Stroop tasks using variants with increasing interference levels - word-reading, ink-naming, and the switching of the two tasks, combined in a systematic dual-tasking fashion with three motor conditions - sitting, standing, and treadmill walking. Neurodynamics underlying interference control were recorded using the electroencephalogram. Worsened performance was observed for the incongruent compared to congruent trials and for the switching Stroop compared to the other two variants. The early frontocentral event-related potentials (ERPs) associated with executive functions (P2, N2) differentially signaled posture-related workloads, while the later stages of information processing indexed faster interference suppression and response selection in walking compared to static conditions. The early P2 and N2 components as well as frontocentral Theta and parietal Alpha power were sensitive to increasing workloads on the motor and cognitive systems. The distinction between the type of load (motor and cognitive) became evident only in the later posterior ERP components in which the amplitude non-uniformly reflected the relative attentional demand of a task. Our data suggest that walking might facilitate selective attention and interference control in healthy adults. Existing interpretations of ERP components recorded in stationary settings should be considered with care as they might not be directly transferable to mobile settings.

Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review

Research on embodiment of objects external to the human body has revealed important information about how the human nervous system interacts with robotic lower limb exoskeletons. Typical robotic exoskeleton control approaches view the controllers as an external agent intending to move in coordination with the human. However, principles of embodiment suggest that the exoskeleton controller should ideally coordinate with the human such that the nervous system can adequately model the input-output dynamics of the exoskeleton controller. Measuring embodiment of exoskeletons should be a necessary step in the exoskeleton development and prototyping process. Researchers need to establish high fidelity quantitative measures of embodiment, rather than relying on current qualitative survey measures. Mobile brain imaging techniques, such as high-density electroencephalography, is likely to provide a deeper understanding of embodiment during human-machine interactions and advance exoskeleton research and development. In this review we show why future exoskeleton research should include quantitative measures of embodiment as a metric of success.

Does Standing Up Enhance Performance on the Stroop Task in Healthy Young Adults? A Systematic Review and Meta-Analysis

Understanding the changes in cognitive processing that accompany changes in posture can expand our understanding of embodied cognition and open new avenues for applications in (neuro)ergonomics. Recent studies have challenged the question of whether standing up alters cognitive performance. An electronic database search for randomized controlled trials was performed using Academic Search Complete, CINAHL Ultimate, MEDLINE, PubMed, and Web of Science following PRISMA guidelines, PICOS framework, and standard quality assessment criteria (SQAC). We pooled data from a total of 603 healthy young adults for incongruent and 578 for congruent stimuli and Stroop effect (mean age = 24 years). Using random-effects results, no difference was found between sitting and standing for the Stroop effect (Hedges' g = 0.13, 95% CI = -0.04 to 0.29, p = 0.134), even when comparing congruent (Hedges' g = 0.10; 95% CI: -0.132 to 0.339; Z = 0.86; p = 0.389) and incongruent (Hedges' g = 0.18; 95% CI: -0.072 to 0.422; Z = 1.39; p = 0.164) stimuli separately. Importantly, these results imply that changing from a seated to a standing posture in healthy young adults is unlikely to have detrimental effects on selective attention and cognitive control. To gain a full understanding of this phenomenon, further research should examine this effect in a population of healthy older adults, as well as in a population with pathology.

Virtual Reality for Spatial Navigation

Immersive virtual reality (VR) allows its users to experience physical space in a non-physical world. It has developed into a powerful research tool to investigate the neural basis of human spatial navigation as an embodied experience. The task of wayfinding can be carried out by using a wide range of strategies, leading to the recruitment of various sensory modalities and brain areas in real-life scenarios. While traditional desktop-based VR setups primarily focus on vision-based navigation, immersive VR setups, especially mobile variants, can efficiently account for motor processes that constitute locomotion in the physical world, such as head-turning and walking. When used in combination with mobile neuroimaging methods, immersive VR affords a natural mode of locomotion and high immersion in experimental settings, designing an embodied spatial experience. This in turn facilitates ecologically valid investigation of the neural underpinnings of spatial navigation.

Music and Brain Circuitry: Strategies for Strengthening Evidence-Based Research for Music-Based Interventions

The neuroscience of music and music-based interventions (MBIs) is a fascinating but challenging research field. While music is a ubiquitous component of every human society, MBIs may encompass listening to music, performing music, music-based movement, undergoing music education and training, or receiving treatment from music therapists. Unraveling the brain circuits activated and influenced by MBIs may help us gain better understanding of the therapeutic and educational values of MBIs by gathering strong research evidence. However, the complexity and variety of MBIs impose unique research challenges. This article reviews the recent endeavor led by the National Institutes of Health to support evidence-based research of MBIs and their impact on health and diseases. It also highlights fundamental challenges and strategies of MBI research with emphases on the utilization of animal models, human brain imaging and stimulation technologies, behavior and motion capturing tools, and computational approaches. It concludes with suggestions of basic requirements when studying MBIs and promising future directions to further strengthen evidence-based research on MBIs in connections with brain circuitry.SIGNIFICANCE STATEMENT Music and music-based interventions (MBI) engage a wide range of brain circuits and hold promising therapeutic potentials for a variety of health conditions. Comparative studies using animal models have helped in uncovering brain circuit activities involved in rhythm perception, while human imaging, brain stimulation, and motion capture technologies have enabled neural circuit analysis underlying the effects of MBIs on motor, affective/reward, and cognitive function. Combining computational analysis, such as prediction method, with mechanistic studies in animal models and humans may unravel the complexity of MBIs and their effects on health and disease.

Dynamic changes of brain networks during standing balance control under visual conflict

Stance balance control requires a very accurate tuning and combination of visual, vestibular, and proprioceptive inputs, and conflict among these sensory systems may induce posture instability and even falls. Although there are many human mechanics and psychophysical studies for this phenomenon, the effects of sensory conflict on brain networks and its underlying neural mechanisms are still unclear. Here, we combined a rotating platform and a virtual reality (VR) headset to control the participants’ physical and visual motion states, presenting them with incongruous (sensory conflict) or congruous (normal control) physical-visual stimuli. Further, to investigate the effects of sensory conflict on stance stability and brain networks, we recorded and calculated the effective connectivity of source-level electroencephalogram (EEG) and the average velocity of the plantar center of pressure (COP) in healthy subjects (18 subjects: 10 males, 8 females). First, our results showed that sensory conflict did have a detrimental effect on stance posture control [sensor F(1, 17) = 13.34, P = 0.0019], but this effect decreases over time [window*sensor F(2, 34) = 6.72, P = 0.0035]. Humans show a marked adaptation to sensory conflict. In addition, we found that human adaptation to the sensory conflict was associated with changes in the cortical network. At the stimulus onset, congruent and incongruent stimuli had similar effects on brain networks. In both cases, there was a significant increase in information interaction centered on the frontal cortices (p < 0.05). Then, after a time window, synchronized with the restoration of stance stability under conflict, the connectivity of large brain regions, including posterior parietal, visual, somatosensory, and motor cortices, was generally lower in sensory conflict than in controls (p < 0.05). But the influence of the superior temporal lobe on other cortices was significantly increased. Overall, we speculate that a posterior parietal-centered cortical network may play a key role in integrating congruous sensory information. Furthermore, the dissociation of this network may reflect a flexible multisensory interaction strategy that is critical for human posture balance control in complex and changing environments. In addition, the superior temporal lobe may play a key role in processing conflicting sensory information.

Predicting the Quality of Spatial Learning via Virtual Global Landmarks

Analyzing the effects landmarks have on spatial learning is an active area of research in the study of human navigation processes and one that is key to understanding the links between human brain dynamics, landmark encoding, and spatial learning outcomes. This article presents a study on whether electroencephalography (EEG) signals related to virtual global landmarks combined with deep learning can be used to predict the accuracy and efficacy of spatial learning. Virtual global landmarks are silhouettes of actual landmarks projected into the navigator's vision via a heads-up display. They serve as a notable frame of reference in addition to the local landmarks we all typically use for route navigation. From a mobile virtual reality scenario involving 55 participants, the results of the study suggest that the EEG data associated with those who were exposed to global landmarks shows a visibly better capacity for predicting the quality of spatial learning levels than those who were not. As such, the EEG features associated with processing VGLs have a greater functional relation to the quality of spatial learning. This finding opens up a future direction of enquiry into landmark encoding and navigational ability. It may also provide a potential avenue for the early diagnosis of Alzheimer's disease.

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.

Cortical Correlates of Increased Postural Task Difficulty in Young Adults: A Combined Pupillometry and EEG Study

The pupillary response reflects mental effort (or cognitive workload) during cognitive and/or motor tasks including standing postural control. EEG has been shown to be a non-invasive measure to assess the cortical involvement of postural control. The purpose of this study was to understand the effect of increasing postural task difficulty on the pupillary response and EEG outcomes and their relationship in young adults. Fifteen adults completed multiple trials of standing: eyes open, eyes open while performing a dual-task (auditory two-back), eyes occluded, and eyes occluded with a dual-task. Participants stood on a force plate and wore an eye tracker and 256-channel EEG cap during the conditions. The power spectrum was analyzed for absolute theta (4−7 Hz), alpha (8−13 Hz), and beta (13−30 Hz) frequency bands. Increased postural task difficulty was associated with greater pupillary response (p < 0.001) and increased posterior region alpha power (p = 0.001) and fronto-central region theta/beta power ratio (p = 0.01). Greater pupillary response correlated with lower posterior EEG alpha power during eyes-occluded standing with (r = −0.67, p = 0.01) and without (r = −0.69, p = 0.01) dual-task. A greater pupillary response was associated with lower CoP displacement in the anterior−posterior direction during dual-task eyes-occluded standing (r = −0.60, p = 0.04). The pupillary response and EEG alpha power appear to capture similar cortical processes that are increasingly utilized during progressively more challenging postural task conditions. As the pupillary response also correlated with task performance, this measurement may serve as a valuable stand-alone or adjunct tool to understand the underlying neurophysiological mechanisms of postural control.

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.

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.

The Embodiment of Architectural Experience: A Methodological Perspective on Neuro-Architecture

People spend a large portion of their time inside built environments. Research in neuro-architecture-the neural basis of human perception of and interaction with the surrounding architecture-promises to advance our understanding of the cognitive processes underlying this common human experience and also to inspire evidence-based architectural design principles. This article examines the current state of the field and offers a path for moving closer to fulfilling this promise. The paper is structured in three sections, beginning with an introduction to neuro-architecture, outlining its main objectives and giving an overview of experimental research in the field. Afterward, two methodological limitations attending current brain-imaging architectural research are discussed: the first concerns the limited focus of the research, which is often restricted to the aesthetic dimension of architectural experience; the second concerns practical limitations imposed by the typical experimental tools and methods, which often require participants to remain stationary and prevent naturalistic interaction with architectural surroundings. Next, we propose that the theoretical basis of ecological psychology provides a framework for addressing these limitations and motivates emphasizing the role of embodied exploration in architectural experience, which encompasses but is not limited to aesthetic contemplation. In this section, some basic concepts within ecological psychology and their convergences with architecture are described. Lastly, we introduce Mobile Brain/Body Imaging (MoBI) as one emerging brain imaging approach with the potential to improve the ecological validity of neuro-architecture research. Accordingly, we suggest that combining theoretical and conceptual resources from ecological psychology with state-of-the-art neuroscience methods (Mobile Brain/Body Imaging) is a promising way to bring neuro-architecture closer to accomplishing its scientific and practical goals.

Using virtual global landmark to improve incidental spatial learning

To reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.

Neural Sources of Prediction Errors Detect Unrealistic VR Interactions

Objective Neural interfaces hold significant promise to implicitly track user experience. Their application in VR/AR simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach We present a complimentary metric based on a ERPs. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe EEG and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main Results Using prediction error negativity features, we classified VR glitches with ~77\% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.

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.

Changes in brain activity of trainees during laparoscopic surgical virtual training assessed with electroencephalography

OBJECTIVE

Evaluate changes in brain activity of trainees during laparoscopic surgical training from electroencephalographic (EEG) signals in an ecological scenario with few restrictions for the user. Design Longitudinal study with two follow-up measurements in the first and last session of a 4-week training with LapSim laparoscopic surgery simulator. Variables analyzed include EEG neuronal activations in theta and alpha bands, tasks performance measures, and subjective measures such as perception of mental workload. Setting Medical School, Universidad de Antioquia, Medellin, Colombia. Participants First-year surgical residents (n = 16, age = 28.0 ± 2.6 years old, right-handed, 9 females) RESULTS: Significant improvements in tasks performance were found together with changes in neuronal activity over frontal and parietal cortex. These changes were also correlated with task performance through training sessions.

CONCLUSIONS

The use of neurophysiological measures such as electroencephalography combined with source separation techniques allows evaluating neural changes associated with motor training. The experiment proposed in this work establishes less controlled recording conditions leading to a more realistic analysis scenario to cognitive assessment in residents training.

Removal of movement-induced EEG artifacts: current state of the art and guidelines

Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.

Future trends in brain aging research: Visuo-cognitive functions at stake during mobility and spatial navigation

Aging leads to a complex pattern of structural and functional changes, gradually affecting sensorimotor, perceptual, and cognitive processes. These multiscale changes can hinder older adults' interaction with their environment, progressively reducing their autonomy in performing tasks relevant to everyday life. Autonomy loss can further be aggravated by the onset and progression of neurodegenerative disorders (e.g., age-related macular degeneration at the sensory input level; and Alzheimer's disease at the cognitive level). In this context, spatial cognition offers a representative case of high-level brain function that involves multimodal sensory processing, postural control, locomotion, spatial orientation, and wayfinding capabilities. Hence, studying spatial behavior and its neural bases can help identify early markers of pathogenic age-related processes. Until now, the neural correlates of spatial cognition have mostly been studied in static conditions thereby disregarding perceptual (other than visual) and motor aspects of natural navigation. In this review, we first demonstrate how visuo-motor integration and the allocation of cognitive resources during locomotion lie at the heart of real-world spatial navigation. Second, we present how technological advances such as immersive virtual reality and mobile neuroimaging solutions can enable researchers to explore the interplay between perception and action. Finally, we argue that the future of brain aging research in spatial navigation demands a widespread shift toward the use of naturalistic, ecologically valid experimental paradigms to address the challenges of mobility and autonomy decline across the lifespan.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

Dual-Task Performance in Hearing-Impaired Older Adults-Study Protocol for a Cross-Sectional Mobile Brain/Body Imaging Study

Background: Hearing impairments are associated with reduced walking performance under Dual-task (DT) conditions. Little is known about the neural representation of DT performance while walking in this target group compared to healthy controls or younger adults. Therefore, utilizing the Mobile Brain/Body Imaging approach (MoBI), we aim at gaining deeper insights into the brain dynamics underlying the interaction of cognitive and motor processes during different DT conditions (visual and auditory) controlling for age and the potential performance decrements of older adults with hearing impairments.

Methods: The cross-sectional study integrates a multifactorial mixed-measure design. Between-subject factors grouping the sample will be age (younger vs. older adults) and hearing impairment (mild vs. not hearing impaired). The within-subject factors will be the task complexity (single- vs. DT) and cognitive task modality (visual vs. auditory). Stimuli of the cognitive task will vary according to the stimulus modality (visual vs. auditory), presentation side (left vs. right), and presentation-response compatibility (ipsilateral vs. contralateral). Analyses of DT costs and underlying neuronal correlates focus either on gait or cognitive performance. Based on an a priori sample size calculation 96 (48 healthy and 48 mildly hearing impaired) community-dwelling older adults (50–70 years) and 48 younger adults (20–30 years) will be recruited. Gait parameters of speed and rhythm will be captured. EEG activity will be recorded using 64 active electrodes.

Discussion: The study evaluates cognitive-motor interference (CMI) in groups of young and older adults as well as older adults with hearing impairment. The underlying processes of the interaction between motor and cognitive tasks will be identified at a behavioral and neurophysiological level comparing an auditory or a visual secondary task. We assume that performance differences are linked to different cognitive-motor processes, i.e., stimulus input, resource allocation, and movement execution. Moreover, for the different DT conditions (auditory vs. visual) we assume performance decrements within the auditory condition, especially for older, hearing-impaired adults. Findings will provide evidence of general mechanisms of CMI (ST vs. DT walking) as well as task-specific effects in dual-task performance while over ground walking.

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

Using mobile brain/body imaging to advance research in arts, health, and related therapeutics

The uses of mobile brain/body imaging (MoBI) are expanding and allow for more direct study of the neurophysiological signals associated with behavior in psychotherapeutic encounters. Neuroaesthetics is concerned with the cognitive and neural basis of art appreciation, and scientific correlations are being made in the field that might help to clarify theories claimed in the creative arts therapies. Yet, most neuroaesthetics studies are confined to the laboratory and do not propose a translation for research methods and clinical applications. The creative arts therapies have a long history of clinical success with various patient populations and will benefit from increased scientific explanation to support intervention strategies. Examining the brain dynamics and motor behaviors that are associated with the higher complex processes involved in artistic expression offers MoBI as a promising instrumentation to move forward in linking ideas from neuroaesthetics to the creative arts therapies. Tracking brain dynamics in association with behavioral change allows for more objective and quantitative physiological monitors to evaluate, and together with subjective patient reports provides insight into the psychological mechanisms of change in treatment. We outline a framework that shows how MoBI can be used to study the effectiveness of creative arts therapy interventions motivated by the 4E approach to cognition with a focus on visual art therapy. The article illuminates how a new partnership among the fields of art therapy, neuroscience, and neuroaesthetics might work together within the 4E/MoBI framework in efforts to advance transdisciplinary research for clinical health populations.

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.

Integrated Gait Triggered Mixed Reality and Neurophysiological Monitoring as a Framework for Next-Generation Ambulatory Stroke Rehabilitation

Brain stroke affects millions of people in the world every year, with 50 to 60 percent of stroke survivors suffering from functional disabilities, for which early and sustained post-stroke rehabilitation is highly recommended. However, approximately one third of stroke patients do not receive early in hospital rehabilitation programs due to insufficient medical facilities or lack of motivation. Gait triggered mixed reality (GTMR) is a cognitive-motor dual task with multisensory feedback tailored for lower-limb post-stroke rehabilitation, which we propose as a potential method for addressing these rehabilitation challenges. Simultaneous gait and EEG data from nine stroke patients was recorded and analyzed to assess the applicability of GTMR to different stroke patients, determine any impacts of GTMR on patients, and better understand brain dynamics as stroke patients perform different rehabilitation tasks. Walking cadence improved significantly for stroke patients and lower-limb movement induced alpha band power suppression during GTMR tasks. The brain dynamics and gait performance across different severities of stroke motor deficits was also assessed; the intensity of walking induced event related desynchronization (ERD) was found to be related to motor deficits, as classified by Brunnstrom stage. In particular, stronger lower-limb movement induced ERD during GTMR rehabilitation tasks was found for patients with moderate motor deficits (Brunnstrom stage IV). This investigation demonstrates the efficacy of the GTMR paradigm for enhancing lower-limb rehabilitation, explores the neural activities of cognitive-motor tasks in different stages of stroke, and highlights the potential for joining enhanced rehabilitation and real-time neural monitoring for superior stroke rehabilitation.

Decoding subjective emotional arousal from EEG during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a long short-term memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world 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.

EEG beta-modulations reflect age-specific motor resource allocation during dual-task walking

The parallel execution of two motor tasks can lead to performance decrements in either one or both of the tasks. Age-related declines can further magnify the underlying competition for cognitive resources. However, little is known about the neural dynamics underlying motor resource allocation during dual-task walking. To better understand motor resource conflicts, this study investigated sensorimotor brain rhythms in younger and older adults using a dual-task protocol. Time-frequency data from two independent component motor clusters were extracted from electroencephalography data during sitting and walking with an additional task requiring manual responses. Button press-related desynchronization in the alpha and beta frequency range were analyzed for the impact of age (< 35 years, ≥ 70 years) and motor task (sitting, walking). Button press-related desynchronization in the beta band was more pronounced for older participants and both age groups demonstrated less pronounced desynchronizations in both frequency bands during walking compared to sitting. Older participants revealed less power modulations between sitting and walking, and less pronounced changes in beta and alpha suppression were associated with greater slowing in walking speed. Our results indicate age-specific allocations strategies during dual-task walking as well as interdependencies of concurrently performed motor tasks reflected in modulations of sensorimotor rhythms.