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

Auditory associative word learning in adults: The effects of musical experience and stimulus ordering

Evidence for sequential associative word learning in the auditory domain has been identified in infants, while adults have shown difficulties. To better understand which factors may facilitate adult auditory associative word learning, we assessed the role of auditory expertise as a learner-related property and stimulus order as a stimulus-related manipulation in the association of auditory objects and novel labels. We tested in the first experiment auditorily-trained musicians versus athletes (high-level control group) and in the second experiment stimulus ordering, contrasting object-label versus label-object presentation. Learning was evaluated from Event-Related Potentials (ERPs) during training and subsequent testing phases using a cluster-based permutation approach, as well as accuracy-judgement responses during test. Results revealed for musicians a late positive component in the ERP during testing, but neither an N400 (400-800 ms) nor behavioral effects were found at test, while athletes did not show any effect of learning. Moreover, the object-label-ordering group only exhibited emerging association effects during training, while the label-object-ordering group showed a trend-level late ERP effect (800-1200 ms) during test as well as above chance accuracy-judgement scores. Thus, our results suggest the learner-related property of auditory expertise and stimulus-related manipulation of stimulus ordering modulate auditory associative word learning in adults.

Spatial Contextual Information Modulates Affordance Processing and Early Electrophysiological Markers of Scene Perception

Scene perception allows humans to extract information from their environment and plan navigation efficiently. The automatic extraction of potential paths in a scene, also referred to as navigational affordance, is supported by scene-selective regions (SSRs) that enable efficient human navigation. Recent evidence suggests that the activity of these SSRs can be influenced by information from adjacent spatial memory areas. However, it remains unexplored how this contextual information could influence the extraction of bottom-up information, such as navigational affordances, from a scene and the underlying neural dynamics. Therefore, we analyzed ERPs in 26 young adults performing scene and spatial memory tasks in artificially generated rooms with varying numbers and locations of available doorways. We found that increasing the number of navigational affordances only impaired performance in the spatial memory task. ERP results showed a similar pattern of activity for both tasks, but with increased P2 amplitude in the spatial memory task compared with the scene memory. Finally, we reported no modulation of the P2 component by the number of affordances in either task. This modulation of early markers of visual processing suggests that the dynamics of SSR activity are influenced by a priori knowledge, with increased amplitude when participants have more contextual information about the perceived scene. Overall, our results suggest that prior spatial knowledge about the scene, such as the location of a goal, modulates early cortical activity associated with SSRs, and that this information may interact with bottom-up processing of scene content, such as navigational affordances.

Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality

Monitoring the reality status of conscious experience is essential for a human being to interact successfully with the external world. Despite its importance for everyday functioning, reality monitoring can systematically become erroneous, for example, while dreaming or during hallucinatory experiences. To investigate brain processes associated with reality monitoring occurring online during an experience, i.e., perceptual reality monitoring, we assessed EEG microstates in healthy, young participants. In a within-subjects design, we compared the experience of reality when being confronted with dream-like bizarre elements versus realistic elements in an otherwise highly naturalistic real-world scenario in immersive virtual reality. Dream-like bizarreness induced changes in the subjective experience of reality and bizarreness, and led to an increase in the contribution of a specific microstate labelled C'. Microstate C' was related to the suspension of disbelief, i.e. the suppression of bizarre mismatches. Together with the functional interpretation of microstate C' as reported by previous studies, the findings of this study point to the importance of prefrontal meta-conscious control processes in perceptual reality monitoring.

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Neural encoding of linguistic speech cues is unaffected by cognitive decline, but decreases with increasing hearing impairment

The multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of cognitive decline in older adults, taking into account the effects of hearing. Participants without ( n = 25 ) and with ( n = 19 ) early signs of cognitive decline listened to an audiobook while their electroencephalography responses were recorded. Using the mTRF framework, we modeled the relationship between speech input and neural response via different acoustic, segmented and linguistic encoding models and examined the response functions in terms of encoding accuracy, signal power, peak amplitudes and latencies. Our results showed no significant effect of cognitive decline or hearing ability on the neural encoding of acoustic and linguistic speech features. However, we found a significant interaction between hearing ability and the word-level segmentation model, suggesting that hearing impairment specifically affects encoding accuracy for this model, while other features were not affected by hearing ability. These results suggest that while speech processing markers remain unaffected by cognitive decline and hearing loss per se, neural encoding of word-level segmented speech features in older adults is affected by hearing loss but not by cognitive decline. This study emphasises the effectiveness of mTRF analysis in studying the neural encoding of speech and argues for an extension of research to investigate its clinical impact on hearing loss and cognition.

Attentional demands in the visual field modulate audiovisual interactions in the temporal domain

Attention and crossmodal interactions are closely linked through a complex interplay at different stages of sensory processing. Within the context of motion perception, previous research revealed that attentional demands alter audiovisual interactions in the temporal domain. In the present study, we aimed to understand the neurophysiological correlates of these attentional modulations. We utilized an audiovisual motion paradigm that elicits auditory time interval effects on perceived visual speed. The audiovisual interactions in the temporal domain were quantified by changes in perceived visual speed across different auditory time intervals. We manipulated attentional demands in the visual field by having a secondary task on a stationary object (i.e., single- vs. dual-task conditions). When the attentional demands were high (i.e., dual-task condition), there was a significant decrease in the effects of auditory time interval on perceived visual speed, suggesting a reduction in audiovisual interactions. Moreover, we found significant differences in both early and late neural activities elicited by visual stimuli across task conditions (single vs. dual), reflecting an overall increase in attentional demands in the visual field. Consistent with the changes in perceived visual speed, the audiovisual interactions in neural signals declined in the late positive component range. Compared with the findings from previous studies using different paradigms, our findings support the view that attentional modulations of crossmodal interactions are not unitary and depend on task-specific components. They also have important implications for motion processing and speed estimation in daily life situations where sensory relevance and attentional demands constantly change.

Human brain dynamics dissociate early perceptual and late motor-related stages of affordance processing

Affordances, the opportunities for action offered by the environment to an agent, are vital for meaningful behaviour and exist in every interaction with the environment. There is an ongoing debate in the field about whether the perception of affordances is an automated process. Some studies suggest that affordance perception is an automated process that is independent from the visual context and bodily interaction with the environment, whereas others argue that it is modulated by the visual and motor context in which affordances are perceived. The present paper aims to resolve this debate by examining affordance automaticity from the perspective of sensorimotor time windows. To investigate the impact of different forms of bodily interactions with an environment, that is, the movement context (physical vs. joystick movement), we replicated a previous study on affordance perception in which participants actively moved through differently wide doors in an immersive 3D virtual environment. In the present study, we displayed the same environment on a 2D screen with participants moving through doors of different widths using the keys on a standard keyboard. We compared components of the event-related potential (ERP) from the continuously recorded electroencephalogram (EEG) that were previously reported to be related to affordance perception of architectural transitions (passable and impassable doors). Comparing early sensory and later motor-related ERPs, our study replicated ERPs reflecting early affordance perception but found differences in later motor-related components. These results indicate a shift from automated perception of affordances during early sensorimotor time windows to movement context dependence of affordance perception at later stages, suggesting that affordance perception is a dynamic and flexible process that changes over sensorimotor stages.

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.

Commit to your putting stroke: exploring the impact of quiet eye duration and neural activity on golf putting performance

Introduction

There is a growing interest in characterizing the cognitive-motor processes that underlie superior performance in highly skilled athletes. The aim of this study was to explore neural markers of putting performance in highly skilled golfers by recording mobile EEG (electroencephalogram) during the pre-shot period.

Methods

Twenty-eight right-handed participants (20 males) with a mean age of 24.2 years (± 6.4) and an average handicap of +1.7 (± 6.4) completed a testing session. Following the warm-up, participants completed 140 putts from a distance of 8ft (2.4m), with putts taken from 5 different positions. While putting, participants wore an eye tracker and a gel-based EEG system with 32 electrodes. Time and frequency domain features of the EEG signals were extracted to characterize Movement-Related Cortical Potentials (MRCP) and rhythmic modulations of neural activity in theta, alpha, sensorimotor and beta frequency bands associated with putting performance.

Results

Eye-tracking data demonstrate that mean Quiet Eye durations are not a reliable marker of expertise as the same duration was found for both successful and unsuccessful putts. Following rigorous data processing data from 12 participants (8 males, mean age 21.6 years ± 5.4, average handicap +1.5 ± 4.4) were included in the EEG analysis. MRCP analysis revealed performance-based differences, with unsuccessful putts having a greater negative amplitude in comparison to successful putts. Time frequency analysis of the EEG data revealed that successful putts exhibit distinct neural activity profiles compared to unsuccessful ones. For successful putts, greater suppression of beta was present in the central region prior to the putt. By contrast, increased frontal theta power was present for unsuccessful putts immediately before the putt (consistent with hesitation and the need for motor plan adjustments prior to execution).

Discussion

We propose that neural activity may provide plausible insights into the mechanisms behind why identical QE durations can lead to both success and failure. From an applied perspective, this study highlights the merits of a multi-measure approach to gain further insights into performance differences within highly skilled golfers. We discuss considerations for future research and solutions to address the challenges related to the complexities of collecting clean EEG signals within naturalistic sporting contexts.

Oscillatory Neural Correlates of Police Firearms Decision-Making in Virtual Reality

We investigated the neural signatures of expert decision-making in the context of police training in a virtual reality-based shoot/don't shoot scenario. Police officers can use stopping force against a perpetrator, which may require using a firearm and each decision made by an officer to discharge their firearm or not has substantial implications. Therefore, it is important to understand the cognitive and underlying neurophysiological processes that lead to such a decision. We used virtual reality-based simulations to elicit ecologically valid behavior from authorized firearms officers (AFOs) in the UK and matched novices in a shoot/don't shoot task and recorded electroencephalography concurrently. We found that AFOs had consistently faster response times than novices, suggesting our task was sensitive to their expertise. To investigate differences in decision-making processes under varying levels of threat and expertise, we analyzed electrophysiological signals originating from the anterior cingulate cortex. In line with similar response inhibition tasks, we found greater increases in preresponse theta power when participants inhibited the response to shoot when under no threat as compared with shooting. Most importantly, we showed that when preparing against threat, theta power increase was greater for experts than novices, suggesting that differences in performance between experts and novices are due to their greater orientation toward threat. Additionally, shorter beta rebounds suggest that experts were "ready for action" sooner. More generally, we demonstrate that the investigation of expert decision-making should incorporate naturalistic stimuli and an appropriate control group to enhance validity.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA

Introduction

To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp.

Methods

A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment.

Results

Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects.

Discussion

This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

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.

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

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

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

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

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.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods in minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

Distinct Cortical Correlates of Perception and Motor Function in Balance Control

Fluctuations in brain activity alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance, we recently showed that evoked brain activity is associated with the balance ability in young individuals. Furthermore, in PD, impaired whole-body motion perception in reactive balance is associated with impaired balance. Here, we investigated the brain activity during the whole-body motion perception in reactive balance in young adults (9 female, 10 male). We hypothesized that both ongoing and evoked cortical activity influences the efficiency of information processing for successful perception and movement during whole-body behaviors. We characterized two cortical signals using electroencephalography localized to the SMA: (1) the "N1," a perturbation-evoked potential that decreases in amplitude with expectancy and is larger in individuals with lower balance function, and (2) preperturbation β power, a transient rhythm that favors maintenance of the current sensorimotor state and is inversely associated with tactile perception. In a two-alternative forced choice task, participants judged whether pairs of backward support surface perturbations during standing were in the "same" or "different" direction. As expected, lower whole-body perception was associated with lower balance ability. Within a perturbation pair, N1 attenuation was larger on correctly perceived trials and associated with better balance, but not perception. In contrast, preperturbation β power was higher on incorrectly perceived trials and associated with poorer perception, but not balance. Together, ongoing and evoked cortical activity have unique roles in information processing that give rise to distinct associations with perceptual and balance ability.

Fixation-related potentials during mobile map assisted navigation in the real world: The effect of landmark visualization style

An often-proposed enhancement for mobile maps to aid assisted navigation is the presentation of landmark information, yet understanding of the manner in which they should be displayed is limited. In this study, we investigated whether the visualization of landmarks as 3D map symbols with either an abstract or realistic style influenced the subsequent processing of those landmarks during route navigation. We utilized a real-world mobile electroencephalography approach to this question by combining several tools developed to overcome the challenges typically encountered in real-world neuroscience research. We coregistered eye-movement and EEG recordings from 45 participants as they navigated through a real-world environment using a mobile map. Analyses of fixation event-related potentials revealed that the amplitude of the parietal P200 component was enhanced when participants fixated landmarks in the real world that were visualized on the mobile map in a realistic style, and that frontal P200 latencies were prolonged for landmarks depicted in either a realistic or abstract style compared with features of the environment that were not presented on the map, but only for the male participants. In contrast, we did not observe any significant effects of landmark visualization style on visual P1-N1 peaks or the parietal late positive component. Overall, the findings indicate that the cognitive matching process between landmarks seen in the environment and those previously seen on a map is facilitated by more realistic map display, while low-level perceptual processing of landmarks and recall of associated information are unaffected by map visualization style.

Induced oscillatory brain responses under virtual reality conditions in the context of repetition priming

In the human electroencephalogram (EEG), induced oscillatory responses in various frequency bands are regarded as valuable indices to examine the neural mechanisms underlying human memory. While the advent of virtual reality (VR) drives the investigation of mnemonic processing under more lifelike settings, the joint application of VR and EEG methods is still in its infancy (e.g., due to technical limitations impeding the signal acquisition). The objective of the present EEG study was twofold. First, we examined whether the investigation of induced oscillations under VR conditions yields equivalent results compared to standard paradigms. Second, we aimed at obtaining further insights into basic memory-related brain mechanisms in VR. To these ends, we relied on a standard implicit memory design, namely repetition priming, for which the to-be-expected effects are well-documented for conventional studies. Congruently, we replicated a suppression of the evoked potential after stimulus onset. Regarding the induced responses, we observed a modulation of induced alphaband in response to a repeated stimulus. Importantly, our results revealed a repetition-related suppression of the high-frequency induced gammaband response (>30 Hz), indicating the sharpening of a cortical object representation fostering behavioral priming effects. Noteworthy, the analysis of the induced gammaband responses required a number of measures to minimize the influence of external and internal sources of artefacts (i.e., the electrical shielding of the technical equipment and the control for miniature eye movements). In conclusion, joint VR-EEG studies with a particular focus on induced oscillatory responses offer a promising advanced understanding of mnemonic processing under lifelike conditions.

Predicting and coding sound into action translation in spinal cord injured people

Motor activation in response to perception of action-related stimuli may depend on a resonance mechanism subserving action understanding. The extent to which this mechanism is innate or learned from sensorimotor experience is still unclear. Here, we recorded EEG while people with paraplegia or tetraplegia consequent to spinal cord injury (SCI) and healthy control participants were presented with action sounds produced by body parts (mouth, hands or feet) that were or were not affected by SCI. Non-action sounds were used as further control. We observed reduced brain activation in subjects affected by SCI at both pre- and post-stimulus latencies specifically for those actions whose effector was disconnected by the spinal lesion (i.e., hand sound for tetraplegia and leg sound for both paraplegia and tetraplegia). Correlation analyses showed that these modulations were functionally linked with the chronicity of the lesion, indicating that the longer the time the lesion- EEG data acquisition interval and/or the more the lesion occurred at a young age, the weaker was the cortical activity in response to these action sounds. Tellingly, source estimations confirmed that these modulations originated from a deficit in the motor resonance mechanism, by showing diminished activity in premotor (during prediction and perception) and near the primary motor (during perception) areas. Such dissociation along the cortical hierarchy is consistent with both previous reports in healthy subjects and with hierarchical predictive coding accounts. Overall, these data expand on the notion that sensorimotor experience maintains the cortical representations relevant to anticipate and perceive action-related stimuli.

Somatosensory omissions reveal action-related predictive processing

The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action-induced somatosensory omission responses using EEG in humans. Self-paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus-evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality-unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions. Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3 years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0 kg, 3 kg, and 5 kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale. Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05). Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios.

Amplitude Modulation Perception and Cortical Evoked Potentials in Children With Listening Difficulties and Their Typically Developing Peers

PURPOSE

Amplitude modulations (AMs) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origins of AM processing.

METHOD

Forty-one children with LiD and 44 typically developing children, ages 8-16 years, participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the Listening in Spatialized Noise-Sentences Test (LiSN-S) and a coordinate response measure (CRM)-based task. Evoked responses were obtained during an AM change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (late potential [LP]). Maturational effects were explored via age correlations.

RESULTS

Age correlated with 4-Hz AM thresholds, CRM separated talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4-Hz AM thresholds and area under the LP curve. CRM separated talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the signal-to-noise ratio and phase coherence of the 40-Hz ASSR. AM change response time also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4-Hz thresholds, AM change accuracy, and area under the LP curve.

CONCLUSIONS

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25009103.

Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n=17 healthy volunteers (9 males, 8 females, age 28.66±8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.

Dual-layer electroencephalography data during real-world table tennis

Real-world settings are necessary to improve the ecological validity of neuroscience research, and electroencephalography (EEG) facilitates mobile electrocortical recordings because of its easy portability and high temporal resolution. Table tennis is a whole-body, goal-directed sport that requires constant visuomotor feedback, anticipation, strategic decision-making, object interception, and performance monitoring - making it an interesting testbed for a variety of neuroscience studies. Although traditionally plagued by artifact contamination, recent advances in signal processing and hardware approaches, such as the dual-layer approach, have allowed high fidelity EEG recordings during whole-body maneuvers. Here, we present a dual-layer EEG dataset from 25 healthy human participants playing table tennis with a human opponent and a ball machine. Our dataset includes synchronized, multivariate time series recordings from 120 scalp electrodes, 120 noise electrodes, 8 neck electromyography electrodes, and inertial measurement units on the participant, paddles, and ball machine to record hit events. We also include de-identified T1 anatomical MR images and digitized electrode locations to create subject-specific head models for source localization. In addition, we provide anonymized video recordings and Adobe Premiere project files with hit events labeled (originally used to mark successful/missed hits). Researchers could use the videos to mark their own events of interest. We formatted our dataset in the Brain Imaging Data Structure (BIDS) format to facilitate data reuse and to adhere to the scientific community's new organization standard.

Finding tau rhythms in EEG: An independent component analysis approach

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk

PURPOSE OF REVIEW

In this review, we present recent findings and advancements in the use of neuroimaging to evaluate neural activity relative to ACL injury risk and patellofemoral pain. In particular, we describe prior work using fMRI and EEG that demonstrate the value of these techniques as well as the necessity of continued development in this area. Our goal is to support future work by providing guidance for the successful application of neuroimaging techniques that most effectively expose pain and injury mechanisms.

RECENT FINDINGS

Recent studies that utilized both fMRI and EEG indicate that athletes who are at risk for future ACL injury exhibit divergent brain activity both during active lower extremity movement and at rest. Such activity patterns are likely due to alterations to cognitive, visual, and attentional processes that manifest as coordination deficits during naturalistic movement that may result in higher risk of injury. Similarly, in individuals with PFP altered brain activity in a number of key regions is related to subjective pain judgements as well as measures of fear of movement. Although these findings may begin to allow objective pain assessment and identification, continued refinement is needed. One key limitation across both ACL and PFP related work is the restriction of movement during fMRI and EEG data collection, which drastically limits ecological validity. Given the lack of sufficient research using EEG and fMRI within a naturalistic setting, our recommendation is that researchers target the use of mobile, source localized EEG as a primary methodology for exposing neural mechanisms of ACL injury risk and PFP. Our contention is that this method provides an optimal balance of spatial and temporal resolution with ecological validity via naturalistic movement.

Quantitative Electroencephalography Markers for an Accurate Diagnosis of Frontotemporal Dementia: A Spectral Power Ratio Approach

Background and Objectives: Frontotemporal dementia (FTD) is the second most common form of presenile dementia; however, its diagnosis has been poorly investigated. Previous attempts to diagnose FTD using quantitative electroencephalography (qEEG) have yielded inconsistent results in both spectral and functional connectivity analyses. This study aimed to introduce an accurate qEEG marker that could be used to diagnose FTD and other neurological abnormalities. Materials and Methods: We used open-access electroencephalography data from OpenNeuro to investigate the power ratio between the frontal and temporal lobes in the resting state of 23 patients with FTD and 29 healthy controls. Spectral data were extracted using a fast Fourier transform in the delta (0.5 ≤ 4 Hz), theta (4 ≤ 8 Hz), alpha (8-13 Hz), beta (>13-30 Hz), and gamma (>30-45 Hz) bands. Results: We found that the spectral power ratio between the frontal and temporal lobes is a promising qEEG marker of FTD. Frontal (F)-theta/temporal (T)-alpha, F-alpha/T-theta, F-theta/F-alpha, and T-beta/T-gamma showed a consistently high discrimination score for the diagnosis of FTD for different parameters and referencing methods. Conclusions: The study findings can serve as reference for future research focused on diagnosing FTD and other neurological anomalies.

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

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

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations

Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.

Amplitude modulation perception and cortical evoked potentials in children with listening difficulties and their typically-developing peers

Purpose

Amplitude modulations (AM) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origin of AM processing.

Method

Forty-one children with LiD and forty-four typically-developing children, ages 8-16 y.o., participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the LiSN-S and a Coordinate Response Measure (CRM)-based task. Evoked responses were obtained during an AM Change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (LP). Maturational effects were explored via age correlations.

Results

Age correlated with 4 Hz AM thresholds, CRM Separated Talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4 Hz AM thresholds and area under the LP curve. CRM Separated Talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the SNR and phase coherence of the 40 Hz ASSR. AM Change RT also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4 Hz thresholds, AM Change accuracy, and area under the LP curve.

Conclusions

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

Maintaining Task Performance Levels Under Cognitive Load While Walking Requires Widespread Reallocation of Neural Resources

This study elucidates the neural mechanisms underlying increasing cognitive load while walking by employing 2 versions of a response inhibition task, the '1-back' version and the more cognitively demanding '2-back' version. By using the Mobile Brain/Body Imaging (MoBI) modality, electroencephalographic (EEG) activity, three-dimensional (3D) gait kinematics and task-related behavioral responses were collected while young adults (n = 61) performed either the 1-back or 2-back response inhibition task. Interestingly, increasing inhibitory difficulty from 1-back to 2-back during walking was not associated with any detectable costs in response accuracy, response speed, or gait consistency. However, the more difficult cognitive task was associated with distinct EEG component changes during both successful inhibitions (correct rejections) and successful executions (hits) of the motor response. During correct rejections, ERP changes were found over frontal regions, during latencies related to sensory gain control, conflict monitoring and working memory storage and processing. During hits, ERP changes were found over left-parietal regions during latencies related to orienting attention and subsequent selection and execution of the motor plan. The pattern of attenuation in walking-related EEG amplitude changes, during 2-back task performance, is thought to reflect more effortful recalibration of neural processes, a mechanism which might be a key driver of performance maintenance in the face of increased cognitive demands while walking. Overall, the present findings shed light on the extent of the neurocognitive capacity of young adults and may lead to a better understanding of how factors such as aging or neurological disorders could impinge on this capacity.

Architectural experience influences the processing of others' body expressions

The interplay between space and cognition is a crucial issue in Neuroscience leading to the development of multiple research fields. However, the relationship between architectural space and the movement of the inhabitants and their interactions has been too often neglected, failing to provide a unifying view of architecture's capacity to modulate social cognition broadly. We bridge this gap by requesting participants to judge avatars' emotional expression (high vs. low arousal) at the end of their promenade inside high- or low-arousing architectures. Stimuli were presented in virtual reality to ensure a dynamic, naturalistic experience. High-density electroencephalography (EEG) was recorded to assess the neural responses to the avatar's presentation. Observing highly aroused avatars increased Late Positive Potentials (LPP), in line with previous evidence. Strikingly, 250 ms before the occurrence of the LPP, P200 amplitude increased due to the experience of low-arousing architectures, reflecting an early greater attention during the processing of body expressions. In addition, participants stared longer at the avatar's head and judged the observed posture as more arousing. Source localization highlighted a contribution of the dorsal premotor cortex to both P200 and LPP. In conclusion, the immersive and dynamic architectural experience modulates human social cognition. In addition, the motor system plays a role in processing architecture and body expressions suggesting that the space and social cognition interplay is rooted in overlapping neural substrates. This study demonstrates that the manipulation of mere architectural space is sufficient to influence human social cognition.

Trait and State Anxiety Effects on Mismatch Negativity and Sensory Gating Event-Related Potentials

We used the auditory roving oddball to investigate whether individual differences in self-reported anxiety influence event-related potential (ERP) activity related to sensory gating and mismatch negativity (MMN). The state-trait anxiety inventory (STAI) was used to assess the effects of anxiety on the ERPs for auditory change detection and information filtering in a sample of thirty-six healthy participants. The roving oddball paradigm involves presentation of stimulus trains of auditory tones with certain frequencies followed by trains of tones with different frequencies. Enhanced negative mid-latency response (130-230 ms post-stimulus) was marked at the deviant (first tone) and the standard (six or more repetitions) tone at Fz, indicating successful mismatch negativity (MMN). In turn, the first and second tone in a stimulus train were subject to sensory gating at the Cz electrode site as a response to the second stimulus was suppressed at an earlier latency (40-80 ms). We used partial correlations and analyses of covariance to investigate the influence of state and trait anxiety on these two processes. Higher trait anxiety exhibited enhanced MMN amplitude (more negative) (F(1,33) = 14.259, p = 6.323 × 10-6, ηp2 = 0.302), whereas state anxiety reduced sensory gating (F(1,30) = 13.117, p = 0.001, ηp2 = 0.304). Our findings suggest that high trait-anxious participants demonstrate hypervigilant change detection to deviant tones that appear more salient, whereas increased state anxiety associates with failure to filter out irrelevant stimuli.

Distinct cortical correlates of perception and motor function in balance control

Fluctuations in brain state alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance control, we recently showed that evoked brain activity is associated with balance ability in healthy young individuals. Further, in individuals with Parkinson's disease, impairments in whole-body motion perception in reactive balance are associated with clinical balance impairment. Here we investigated brain activity during whole-body motion perception in reactive balance in healthy young adults. We hypothesized that flexibility in brain states underlies successful perception and movement during whole-body movement. We characterized two cortical sensorimotor signals using electroencephalography localized to the supplementary motor area: 1) the "N1 response", a perturbation-evoked potential that decreases in amplitude with expectancy and is larger in individuals with lower balance function; and 2) pre-perturbation beta oscillatory activity, a rhythm that favors maintenance of the current sensorimotor state and is inversely associated with perception in seated somatosensory perceptual tasks. In a two-alternative forced choice task, participants judged whether pairs of backward support-surface perturbations during standing were in the "same" or "different" direction. As expected, lower whole-body perception was associated with lower balance ability. Within a perturbation pair, N1 attenuation was larger on correctly perceived trials and associated with better balance, but not perception. In contrast, pre-perturbation beta power was higher on incorrectly perceived trials and associated with poorer perception, but not balance. Taken together, flexibility in different cortical processes influences perceptual accuracy but have distinct associations with balance and perceptual ability.

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Depression diagnosis based on electroencephalography power ratios

BACKGROUND

Depression is a common mental disorder that impacts millions of people across the world. However, its diagnosis is difficult due to the dependence on subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study determines whether the alpha/beta (ABR), alpha/theta (ATR), and theta/beta (TBR) ratios can serve as biological markers of depression.

METHODS

We used open-access EEG data from OpenNeuro to investigate power ratios in the resting state of 46 patients with depression and 75 healthy controls. Spectral data were extracted by fast Fourier transform at the theta band (4-8 Hz), alpha band (8-13 Hz), and beta band (13-32 Hz). Neural network, logistic regression, and receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracies of each suggested index. Additionally, the cutoff point, sensitivity, specificity, positive predictive value, and negative predictive value at the maximized Youden index were compared for each variable.

RESULTS

Decreased anterior frontal, frontal, central, parietal, occipital, and temporal ABR and decreased central and parietal TBR were observed in the depression group. The area under the curve of the ROC curves further revealed that these ratios could all effectively differentiate depression. In particular, the central, frontal, and parietal ABR exhibited high discrimination scores. Multiple logistic regression analysis demonstrated that the Beck Depression Inventory and Spielberger Trait Anxiety Inventory scores, as well as the probability of depression, increased with a decrease in the central ABR. Moreover, neural network analysis revealed that the global ABR was the most effective index for diagnosing depression among the three global EEG power ratios.

CONCLUSIONS

The central, frontal, and parietal ABR represent potential biomarkers to differentiate patients with depression from healthy controls.

A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations

Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.

Dry EEG measurement of P3 to evaluate cognitive load during sitting, standing, and walking

Combining brain imaging with dual-task paradigms provides a quantitative, direct metric of cognitive load that is agnostic to the motor task. This work aimed to quantitatively assess cognitive load during activities of daily living-sitting, standing, and walking-using a commercial dry encephalography headset. We recorded participants' brain activity while engaging in a stimulus paradigm that elicited event-related potentials. The stimulus paradigm consisted of an auditory oddball task in which participants had to report the number of oddball tones that were heard during each motor task. We extracted the P3 event-related potential, which is inversely proportional to cognitive load, from EEG signals in each condition. Our main findings showed that P3 was significantly lower during walking compared to sitting (p = .039), suggesting that cognitive load was higher during walking compared to the other activities. There were no significant differences in P3 between sitting and standing. Head motion did not have a significant impact on the measurement of cognitive load. This work validates the use of a commercial dry-EEG headset for measuring cognitive load across different motor tasks. The ability to accurately measure cognitive load in dynamic activities opens new avenues for exploring cognitive-motor interactions in individuals with and without motor impairments. This work highlights the potential of dry EEG for measuring cognitive load in naturalistic settings.

Using event-related brain potentials to evaluate motor-auditory latencies in virtual reality

Actions in the real world have immediate sensory consequences. Mimicking these in digital environments is within reach, but technical constraints usually impose a certain latency (delay) between user actions and system responses. It is important to assess the impact of this latency on the users, ideally with measurement techniques that do not interfere with their digital experience. One such unobtrusive technique is electroencephalography (EEG), which can capture the users' brain activity associated with motor responses and sensory events by extracting event-related potentials (ERPs) from the continuous EEG recording. Here we exploit the fact that the amplitude of sensory ERP components (specifically, N1 and P2) reflects the degree to which the sensory event was perceived as an expected consequence of an own action (self-generation effect). Participants (N = 24) elicit auditory events in a virtual-reality (VR) setting by entering codes on virtual keypads to open doors. In a within-participant design, the delay between user input and sound presentation is manipulated across blocks. Occasionally, the virtual keypad is operated by a simulated robot instead, yielding a control condition with externally generated sounds. Results show that N1 (but not P2) amplitude is reduced for self-generated relative to externally generated sounds, and P2 (but not N1) amplitude is modulated by delay of sound presentation in a graded manner. This dissociation between N1 and P2 effects maps back to basic research on self-generation of sounds. We suggest P2 amplitude as a candidate read-out to assess the quality and immersiveness of digital environments with respect to system latency.

Spontaneous dyadic behaviour predicts the emergence of interpersonal neural synchrony

Synchronization of neural activity across brains - interpersonal neural synchrony (INS) - is emerging as a powerful marker of social interaction that predicts success of multi-person coordination, communication, and cooperation. As the origins of INS are poorly understood, we tested whether and how INS might emerge from spontaneous dyadic behavior. We recorded neural activity (EEG) and human behavior (full-body kinematics, eye movements and facial expressions) while dyads of participants were instructed to look at each other without speaking or making co-verbal gestures. We made four fundamental observations. First, despite the absence of a structured social task, INS emerged spontaneously only when participants were able to see each other. Second, we show that such spontaneous INS, comprising specific spectral and topographic profiles, did not merely reflect intra-personal modulations of neural activity, but it rather reflected real-time and dyad-specific coupling of neural activities. Third, using state-of-art video-image processing and deep learning, we extracted the temporal unfolding of three notable social behavioral cues - body movement, eye contact, and smiling - and demonstrated that these behaviors also spontaneously synchronized within dyads. Fourth, we probed the correlates of INS in such synchronized social behaviors. Using cross-correlation and Granger causality analyses, we show that synchronized social behaviors anticipate and in fact Granger cause INS. These results provide proof-of-concept evidence for studying interpersonal neural and behavioral synchrony under natural and unconstrained conditions. Most importantly, the results suggest that INS could be conceptualized as an emergent property of two coupled neural systems: an entrainment phenomenon, promoted by real-time dyadic behavior.

EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks

Electroencephalography (EEG) is a widely-used neuroimaging technique in Brain Computer Interfaces (BCIs) due to its non-invasive nature, accessibility and high temporal resolution. A range of input representations has been explored for BCIs. The same semantic meaning can be conveyed in different representations, such as visual (orthographic and pictorial) and auditory (spoken words). These stimuli representations can be either imagined or perceived by the BCI user. In particular, there is a scarcity of existing open source EEG datasets for imagined visual content, and to our knowledge there are no open source EEG datasets for semantics captured through multiple sensory modalities for both perceived and imagined content. Here we present an open source multisensory imagination and perception dataset, with twelve participants, acquired with a 124 EEG channel system. The aim is for the dataset to be open for purposes such as BCI related decoding and for better understanding the neural mechanisms behind perception, imagination and across the sensory modalities when the semantic category is held constant.

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

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

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Measurement of the N170 during facial neuromuscular electrical stimulation (fNMES)

BACKGROUND

Studies on facial feedback effects typically employ props or posed facial expressions, which often lack temporal precision and muscle specificity.

NEW METHOD

Facial Neuromuscular Electrical Stimulation (fNMES) allows for a controlled influence of contractions of facial muscles, and may be used to advance our understanding of facial feedback effects, especially when combined with Electroencephalography (EEG). However, electrical stimulation introduces significant interference that can mask underlying brain dynamics. Whether established signal processing methods can allow for a reduction of said interference whilst retaining effects of interest, remains unexplored.

RESULTS

We addressed these questions focusing on the classic N170 visual evoked potential, a face-sensitive brain component: 20 participants viewed images of houses, and of sad, happy, and neutral faces. On half of the trials, fNMES was delivered to bilateral lower-face muscles during the presentation of visual stimuli. A larger N170 amplitude was found for faces relative to houses. Interestingly, this was the case both without and during fNMES, regardless of whether the fNMES artefact was removed or not. Moreover, sad facial expressions elicited a larger N170 amplitude relative to neutral facial expressions, both with and without fNMES.

COMPARISON WITH EXISTING METHODS

fNMES offers a more precise way of manipulating proprioceptive feedback from facial muscles, which affords greater diversity in experimental design for studies on facial feedback effects.

CONCLUSIONS

We show that the combining of fNMES and EEG can be achieved and may serve as a powerful means of exploring the impact of controlled proprioceptive inputs on various types of cognitive processing.

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.