Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion

Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study

BACKGROUND

Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia syndromes. However, not much is known about the neural signature of functional decline, particularly during the very early stages of dementia. Early intervention before overt impairment is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias. However, to justify early intervention, those at risk need to be detected earlier and more accurately. The decline in complex daily function (CdF) such as managing medications has been reported to precede impairment in basic activities of daily living (eg, eating and dressing).

OBJECTIVE

Our goal is to establish the neural signature of decline in CdF during the preclinical dementia period.

METHODS

Gait is central to many CdF and community-based activities. Hence, to elucidate the neural signature of CdF, we validated a novel electroencephalographic approach to measuring gait-related brain activation while participants perform complex gait-based functional tasks. We hypothesize that dementia-related pathology during the preclinical period activates a unique gait-related electroencephalographic (grEEG) pattern that predicts a subsequent decline in CdF.

RESULTS

We provide preliminary findings showing that older adults reporting CdF limitations can be characterized by a unique gait-related neural signature: weaker sensorimotor and stronger motor control activation. This subsample also had smaller brain volume and white matter hyperintensities in regions affected early by dementia and engaged in less physical exercise. We propose a prospective observational cohort study in cognitively unimpaired older adults with and without subclinical AD (plasma amyloid-β) and vascular (white matter hyperintensities) pathologies. We aim to (1) establish the unique grEEG activation as the neural signature and predictor of decline in CdF during the preclinical dementia period; (2) determine associations between dementia-related pathologies and incidence of the neural signature of CdF; and (3) establish associations between a dementia risk factor, physical inactivity, and the neural signature of CdF.

CONCLUSIONS

By establishing the clinical relevance and biological basis of the neural signature of CdF decline, we aim to improve prediction during the preclinical stages of ADs and other dementias. Our approach has important research and translational implications because grEEG protocols are relatively inexpensive and portable, and predicting CdF decline may have real-world benefits.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56726.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

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.

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.

Visual Occlusions Result in Phase Synchrony Within Multiple Brain Regions Involved in Sensory Processing and Balance Control

There is a need to develop appropriate balance training interventions to minimize the risk of falls. Recently, we found that intermittent visual occlusions can substantially improve the effectiveness and retention of balance beam walking practice (Symeonidou & Ferris, 2022). We sought to determine how the intermittent visual occlusions affect electrocortical activity during beam walking. We hypothesized that areas involved in sensorimotor processing and balance control would demonstrate spectral power changes and inter-trial coherence modulations after loss and restoration of vision. Ten healthy young adults practiced walking on a treadmill-mounted balance beam while wearing high-density EEG and experiencing reoccurring visual occlusions. Results revealed spectral power fluctuations and inter-trial coherence changes in the visual, occipital, temporal, and sensorimotor cortex as well as the posterior parietal cortex and the anterior cingulate. We observed a prolonged alpha increase in the occipital, temporal, sensorimotor, and posterior parietal cortex after the occlusion onset. In contrast, the anterior cingulate showed a strong alpha and theta increase after the occlusion offset. We observed transient phase synchrony in the alpha, theta, and beta bands within the sensory, posterior parietal, and anterior cingulate cortices immediately after occlusion onset and offset. Intermittent visual occlusions induced electrocortical spectral power and inter-trial coherence changes in a wide range of frequencies within cortical areas relevant for multisensory integration and processing as well as balance control. Our training intervention could be implemented in senior and rehabilitation centers, improving the quality of life of elderly and neurologically impaired individuals.

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13 Hz) and beta band (13-30 Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7 Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design

OBJECTIVE

We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.

BACKGROUND

Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.

RESULTS

Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior.

CONCLUSION

Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

Ear-EEG sensitivity modeling for neural sources and ocular artifacts

The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.

Experiment protocols for brain-body imaging of locomotion: A systematic review

Introduction

Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings.

Methods

This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols.

Results

The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments.

Discussion

Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.

Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli

While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.

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.

Towards real-world neuroscience using mobile EEG and augmented reality

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

Somatosensory-Evoked Potentials as a Marker of Functional Neuroplasticity in Athletes: A Systematic Review

Background

Somatosensory-evoked potentials (SEP) represent a non-invasive tool to assess neural responses elicited by somatosensory stimuli acquired via electrophysiological recordings. To date, there is no comprehensive evaluation of SEPs for the diagnostic investigation of exercise-induced functional neuroplasticity. This systematic review aims at highlighting the potential of SEP measurements as a diagnostic tool to investigate exercise-induced functional neuroplasticity of the sensorimotor system by reviewing studies comparing SEP parameters between athletes and healthy controls who are not involved in organized sports as well as between athlete cohorts of different sport disciplines.

Methods

A systematic literature search was conducted across three electronic databases (PubMed, Web of Science, and SPORTDiscus) by two independent researchers. Three hundred and ninety-seven records were identified, of which 10 cross-sectional studies were considered eligible.

Results

Differences in SEP amplitudes and latencies between athletes and healthy controls or between athletes of different cohorts as well as associations between SEP parameters and demographic/behavioral variables (years of training, hours of training per week & reaction time) were observed in seven out of 10 included studies. In particular, several studies highlight differences in short- and long-latency SEP parameters, as well as high-frequency oscillations (HFO) when comparing athletes and healthy controls. Neuroplastic differences in athletes appear to be modality-specific as well as dependent on training regimens and sport-specific requirements. This is exemplified by differences in SEP parameters of various athlete populations after stimulation of their primarily trained limb.

Conclusion

Taken together, the existing literature suggests that athletes show specific functional neuroplasticity in the somatosensory system. Therefore, this systematic review highlights the potential of SEP measurements as an easy-to-use and inexpensive diagnostic tool to investigate functional neuroplasticity in the sensorimotor system of athletes. However, there are limitations regarding the small sample sizes and inconsistent methodology of SEP measurements in the studies reviewed. Therefore, future intervention studies are needed to verify and extend the conclusions drawn here.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

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.

Validating Poly(3,4-ethylene dioxythiophene) Polystyrene Sulfonate-Based Textile Electroencephalography Electrodes by a Textile-Based Head Phantom

It is important to go through a validation process when developing new electroencephalography (EEG) electrodes, but it is impossible to keep the human mind constant, making the process difficult. It is also very difficult to identify noise and signals as the input signal is unknown. In this work, we have validated textile-based EEG electrodes constructed from a poly(3,4-ethylene dioxythiophene) polystyrene sulfonate:/polydimethylsiloxane coated cotton fabric using a textile-based head phantom. The performance of the textile-based electrode has also been compared against a commercial dry electrode. The textile electrodes collected a signal to a smaller skin-to-electrode impedance (-18.9%) and a higher signal-to-noise ratio (+3.45%) than Ag/AgCl dry electrodes. From an EEGLAB, it was observed that the inter-trial coherence and event-related spectral perturbation graphs of the textile-based electrodes were identical to the Ag/AgCl electrodes. Thus, these textile-based electrodes can be a potential alternative to monitor brain activity.

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.

Measuring Correlates of Mental Workload During Simulated Driving Using cEEGrid Electrodes: A Test-Retest Reliability Analysis

The EEG reflects mental processes, especially modulations in the alpha and theta frequency bands are associated with attention and the allocation of mental resources. EEG has also been used to study mental processes while driving, both in real environments and in virtual reality. However, conventional EEG methods are of limited use outside of controlled laboratory settings. While modern EEG technologies offer hardly any restrictions for the user, they often still have limitations in measurement reliability. We recently showed that low-density EEG methods using film-based round the ear electrodes (cEEGrids) are well-suited to map mental processes while driving a car in a driving simulator. In the present follow-up study, we explored aspects of ecological and internal validity of the cEEGrid measurements. We analyzed longitudinal data of 127 adults, who drove the same driving course in a virtual environment twice at intervals of 12-15 months while the EEG was recorded. Modulations in the alpha and theta frequency bands as well as within behavioral parameters (driving speed and steering wheel angular velocity) which were highly consistent over the two measurement time points were found to reflect the complexity of the driving task. At the intraindividual level, small to moderate (albeit significant) correlations were observed in about 2/3 of the participants, while other participants showed significant deviations between the two measurements. Thus, the test-retest reliability at the intra-individual level was rather low and challenges the value of the application for diagnostic purposes. However, across all participants the reliability and ecological validity of cEEGrid electrodes were satisfactory in the context of driving-related parameters.

High-speed ocular Artifacts Removal of multichannel EEG Based on improved moment matching

OBJECTIVE

The excellent Signal-to-Noise Ratio (SNR) is the premise of Electroencephalogram (EEG) research and applications. This study aims to use innovative method to swiftly remove the Ocular Artifacts (OAs) from multichannel EEG to enhance the SNR.

METHODS

The moment matching method which is prevalently used to removing stripe noise from hyperspectral images is adapted and improved to deduct OAs from EEG. This modified approach regards sampling points of multichannel EEG as pixels in images. It utilizes the propagation characteristics of EEG to correct the potential shift caused by OAs.

RESULTS

By using mathematical derivation and empirical corroboration, the results suggest that the improved moment matching (IMM) is capable of reducing OAs effectively and reserving the EEG waveform information on the greatest extent compared to existing methods, such as independent component analysis (ICA) and second-order blind identification (SOBI). In the frontal region heavily affected by OAs, the SNR increased by 138.1% compared with ICA, the whole SNR increased by an average of 58.7%. Moreover, low latency superiority is provided for real-time and offline processing.

CONCLUSION

IMM is effective for OAs removal and it is helpful to improve SNR of multichannel EEG.

SIGNIFICANCE

IMM affords option offering preponderance for enhancement of SNR of multichannel EEG.

A Long-Lasting Textile-Based Anatomically Realistic Head Phantom for Validation of EEG Electrodes

During the development of new electroencephalography electrodes, it is important to surpass the validation process. However, maintaining the human mind in a constant state is impossible which in turn makes the validation process very difficult. Besides, it is also extremely difficult to identify noise and signals as the input signals are not known. For that reason, many researchers have developed head phantoms predominantly from ballistic gelatin. Gelatin-based material can be used in phantom applications, but unfortunately, this type of phantom has a short lifespan and is relatively heavyweight. Therefore, this article explores a long-lasting and lightweight (-91.17%) textile-based anatomically realistic head phantom that provides comparable functional performance to a gelatin-based head phantom. The result proved that the textile-based head phantom can accurately mimic body-electrode frequency responses which make it suitable for the controlled validation of new electrodes. The signal-to-noise ratio (SNR) of the textile-based head phantom was found to be significantly better than the ballistic gelatin-based head providing a 15.95 dB ± 1.666 (±10.45%) SNR at a 95% confidence interval.

Mobile brain/body imaging of landmark-based navigation with high-density EEG

Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion‐constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark‐based navigation in actively behaving young adults, solving a Y‐maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state‐of‐the‐art brain imaging literature of landmark‐based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo‐spatial processing and coding, we observed an alpha‐power desynchronization while participants gathered visual information. We also hypothesized behavior‐dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time–frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high‐density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark‐based navigation.

Differential Theta-Band Signatures of the Anterior Cingulate and Motor Cortices During Seated Locomotor Perturbations

Quantifying motor and cortical responses to perturbations during seated locomotor tasks such as recumbent stepping and cycling will expand and improve the understanding of locomotor adaptation processes beyond just perturbed gait. Using a perturbed recumbent stepping protocol, we hypothesized motor errors and anterior cingulate activity would decrease with time, and perturbation timing would influence electrocortical elicitation. Young adults (n = 17) completed four 10-minute arms and legs stepping tasks, with perturbations applied at every left or right leg extension-onset or mid-extension. A random no-perturbation "catch" stride occurred in every five perturbed strides. We instructed subjects to follow a pacing cue and to step smoothly, and we quantified temporal and spatial motor errors. We used high-density electroencephalography to estimate sources of electrocortical fluctuations shared among >70% of subjects. Temporal and spatial errors did not decrease from early to late for either perturbed or catch strides. Interestingly, spatial errors post-perturbation did not return to pre-perturbation levels, suggesting use-dependent learning occurred. Theta (3-8 Hz) synchronization in the anterior cingulate cortex and left and right supplementary motor areas (SMA) emerged near the perturbation event, and extension-onset perturbations elicited greater theta-band power than mid-extension perturbations. Even though motor errors did not adapt, anterior cingulate theta synchronization decreased from early to late perturbed strides, but only during the right-side tasks. Additionally, SMA mainly demonstrated specialized, not contralateral, lateralization. Overall, seated locomotor perturbations produced differential theta-band responses in the anterior cingulate and SMAs, suggesting that tuning perturbation parameters, e.g., timing, can potentially modify electrocortical responses.

A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces

Recently, practical brain-computer interfaces (BCIs) have been widely investigated for detecting human intentions in real world. However, performance differences still exist between the laboratory and the real world environments. One of the main reasons for such differences comes from the user's unstable physical states (e.g., human movements are not strictly controlled), which produce unexpected signal artifacts. Hence, to minimize the performance degradation of electroencephalography (EEG)-based BCIs, we present a novel artifact removal method named constrained independent component analysis with online learning (cIOL). The cIOL can find and reject the noise-like components related to human body movements (i.e., movement artifacts) in the EEG signals. To obtain movement information, isolated electrodes are used to block electrical signals from the brain using high-resistance materials. We estimate artifacts with movement information using constrained independent component analysis from EEG signals and then extract artifact-free signals using online learning in each sample. In addition, the cIOL is evaluated by signal processing under 16 different experimental conditions (two types of EEG devices × two BCI paradigms × four different walking speeds). The experimental results show that the cIOL has the highest accuracy in both scalp- and ear-EEG, and has the highest signal-to-noise ratio in scalp-EEG among the state-of-the-art methods, except for the case of steady-state visual evoked potential at 2.0 m/s with superposition problem.

A Novel Method to Understand Neural Oscillations During Full-Body Reaching: A Combined EEG and 3D Virtual Reality Study

Virtual reality (VR) can be used to create environments that are not possible in the real-world. Producing movements in VR holds enormous promise for rehabilitation and offers a platform from which to understand the neural control of movement. However, no study has examined the impact of a 3D fully immersive head-mounted display (HMD) VR system on the integrity of neural data. We assessed the quality of 64-channel EEG data with and without HMD VR during rest and during a full-body reaching task. We compared resting EEG while subjects completed three conditions: No HMD (EEG-only), HMD powered off (VR-off), and HMD powered on (VR-on). Within the same session, EEG were collected while subjects completed full-body reaching movements in two conditions (EEG-only, VR-on). During rest, no significant differences in data quality and power spectrum were observed between EEG-only, VR-off, and VR-on conditions. During reaching movements, the proportion of components attributed to the brain was greater in the EEG-only condition compared to the VR-on condition. Despite this difference, neural oscillations in source space were not significantly different between conditions, with both conditions associated with decreases in alpha and beta power in sensorimotor cortex during movements. Our findings demonstrate that the integrity of EEG data can be maintained while individuals execute full-body reaching movements within an immersive 3D VR environment. Clinical impact: Integrating VR and EEG is a viable approach to understanding the cortical processes of movement. Simultaneously recording movement and brain activity in combination with VR provides the foundation for neurobiologically informed rehabilitation therapies.

Does the electrode amplification style matter? A comparison of active and passive EEG system configurations during standing and walking

It has been stated that active-transmission electrodes should improve signal quality in mobile EEG recordings. However, few studies have directly compared active- and passive-transmission electrodes during a mobile task. In this repeated measurement study, we investigated the performance of active and passive signal transmission electrodes with the same amplifier system in their respective typical configurations, during a mobile auditory task. The task was an auditory discrimination (1,000 vs. 800 Hz; counterbalanced) oddball task using approximately 560 trials (15% targets) for each condition. Eighteen participants performed the auditory oddball task both while standing and walking in an outdoor environment. While walking, there was a significant decrease in P3 amplitude, post-trial rejection trial numbers, and signal-to-noise ratio (SNR). No significant differences were found in signal quality between the two electrode configurations. SNR and P3 amplitude were test-retest reliable between recordings. We conclude that adequate use of a passive EEG electrode system achieves signal quality equivalent to that of an active system during a mobile task.

Exploring the Limitations of Event-Related Potential Measures in Moving Subjects: Pilot Studies of Four Different Technical Modifications in Ergometer Rowing

Measuring brain activity in moving subjects is of great importance for investigating human behavior in ecological settings. For this purpose, EEG measures are applicable; however, technical modifications are required to reduce the typical massive movement artefacts. Four different approaches to measure EEG/ERPs during rowing were tested: (i) a purpose-built head-mounted preamplifier, (ii) a laboratory system with active electrodes, and a wireless headset combined with (iii) passive or (iv) active electrodes. A standard visual oddball task revealed very similar (within subjects) visual evoked potentials for rowing and rest (without movement). The small intraindividual differences between rowing and rest, in comparison to the typically larger interindividual differences in the ERP waveforms, revealed that ERPs can be measured reliably even in an athletic movement such as rowing. On the other hand, the expected modulation of the motor-related activity by force output was largely affected by movement artefacts. Therefore, for a successful application of ERP measures in movement research, further developments to differentiate between movement-related neuronal activity and movement-related artefacts are required. However, activities with small magnitudes related to motor learning and motor control may be difficult to detect because they are superimposed by the very large motor potential, which increases with force output.

Cognitive-motor interference in the wild: Assessing the effects of movement complexity on task switching using mobile EEG

Adaptively changing between different tasks while in locomotion is a fundamental prerequisite of modern daily life. The cognitive processes underlying dual tasking have been investigated extensively using EEG. Due to technological restrictions, however, this was not possible for dual-task scenarios including locomotion. With new technological opportunities, this became possible and cognitive-motor interference can be studied, even in outside-the-lab environments. In the present study, participants carried out a cognitive-motor interference task as they responded to cued, auditory task-switch stimuli while performing locomotive tasks with increasing complexity (standing, walking, traversing an obstacle course). We observed increased subjective workload ratings as well as decreased behavioural performance for increased movement complexity and cognitive task difficulty. A higher movement load went along with a decrease of parietal P2, N2 and P3 amplitudes and frontal Theta power. A higher cognitive load, on the other hand, was reflected by decreased frontal CNV amplitudes. Additionally, a connectivity analysis using inter-site phase coherence revealed that higher movement as well as cognitive task difficulty had an impairing effect on fronto-parietal connectivity. In conclusion, subjective ratings, behavioural performance and electrophysiological results indicate that less cognitive resources were available to be deployed towards the execution of the cognitive task when in locomotion compared to standing still. Connectivity results also show a scarcity of attentional resources when switching a task during the highest movement complexity condition. Summarized, all findings indicate a central role of attentional control regarding cognitive-motor dual tasking and an inherent limitation of cognitive resources.

Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.

New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

In the literature, it is well established that regular physical exercise is a powerful strategy to promote brain health and to improve cognitive performance. However, exact knowledge about which exercise prescription would be optimal in the setting of exercise–cognition science is lacking. While there is a strong theoretical rationale for using indicators of internal load (e.g., heart rate) in exercise prescription, the most suitable parameters have yet to be determined. In this perspective article, we discuss the role of brain-derived parameters (e.g., brain activity) as valuable indicators of internal load which can be beneficial for individualizing the exercise prescription in exercise–cognition research. Therefore, we focus on the application of functional near-infrared spectroscopy (fNIRS), since this neuroimaging modality provides specific advantages, making it well suited for monitoring cortical hemodynamics as a proxy of brain activity during physical exercise.

Disruptive, Soft, Wearable Sensors

The wearable industry is on the rise, with a myriad of technical applications ranging from real-time health monitoring, the Internet of Things, and robotics, to name but a few. However, there is a saying "wearable is not wearable" because the current market-available wearable sensors are largely bulky and rigid, leading to uncomfortable wearing experience, motion artefacts, and poor data accuracy. This has aroused a world-wide intensive research quest for novel materials, with the aim of fabricating next-generation ultra-lightweight and soft wearable devices. Such disruptive second-skin-like biosensing technologies may enable a paradigm shift from current wearable 1.0 to future wearable 2.0 products. Here, the state-of-the-art progress made in the key phases for future wearable technology, namely, wear → sense → communicate → analyze → interpret → decide, is summarized. Without a doubt, materials innovation is the key, which is the main focus of the discussion. In addition, emphasis is also given to wearable energy, multicomponent integration, and wireless communication.

Neurodiagnostics in Sports: Investigating the Athlete's Brain to Augment Performance and Sport-Specific Skills

Enhancing performance levels of athletes during training and competition is a desired goal in sports. Quantifying training success is typically accompanied by performance diagnostics including the assessment of sports-relevant behavioral and physiological parameters. Even though optimal brain processing is a key factor for augmented motor performance and skill learning, neurodiagnostics is typically not implemented in performance diagnostics of athletes. We propose, that neurodiagnostics via non-invasive brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) will offer novel perspectives to quantify training-induced neuroplasticity and its relation to motor behavior. A better understanding of such a brain-behavior relationship during the execution of sport-specific movements might help to guide training processes and to optimize training outcomes. Furthermore, targeted non-invasive brain stimulation such as transcranial direct current stimulation (tDCS) might help to further enhance training outcomes by modulating brain areas that show training-induced neuroplasticity. However, we strongly suggest that ethical aspects in the use of non-invasive brain stimulation during training and/or competition need to be addressed before neuromodulation can be considered as a performance enhancer in sports.

Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex

OBJECTIVE

Our aim was to determine if walking speed affected human sensorimotor electrocortical dynamics using mobile high-density electroencephalography (EEG).

METHODS

To overcome limitations associated with motion and muscle artifact contamination in EEG recordings, we compared solutions for artifact removal using novel dual-layer EEG electrodes and alternative signal processing methods. Dual-layer EEG simultaneously recorded human electrocortical signals and isolated motion artifacts using pairs of mechanically coupled and electrically independent electrodes. For electrical muscle activity removal, we incorporated electromyographic (EMG) recordings from the neck into our mobile EEG data processing pipeline. We compared artifact removal methods during treadmill walking at four speeds (0.5, 1.0, 1.5, and 2.0 m/s).

RESULTS

Left and right sensorimotor alpha and beta spectral power increased in contralateral limb single support and push off, and decreased during contralateral limb swing at each speed. At faster walking speeds, sensorimotor spectral power fluctuations were less pronounced across the gait cycle with reduced alpha and beta power (p < 0.05) compared to slower speeds. Isolated noise recordings and neck EMG spectral power fluctuations matched gait events and showed broadband spectral power increases at faster speeds.

CONCLUSION AND SIGNIFICANCE

Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds. A comparison of signal processing approaches revealed similar intrastride cortical fluctuations when applying common (e.g., artifact subspace reconstruction) and novel artifact rejection methods. Dual-layer EEG, however, allowed us to document and rule out residual artifacts, which exposed sensorimotor spectral power changes across gait speeds.

Comparison of Signal Processing Methods for Reducing Motion Artifacts in High-Density Electromyography During Human Locomotion

Objective: High-density electromyography (EMG) is useful for studying changes in myoelectric activity within a muscle during human movement, but it is prone to motion artifacts during locomotion. We compared canonical correlation analysis and principal component analysis methods for signal decomposition and component filtering with a traditional EMG high-pass filtering approach to quantify their relative performance at removing motion artifacts from high-density EMG of the gastrocnemius and tibialis anterior muscles during human walking and running. Results: Canonical correlation analysis filtering provided a greater reduction in signal content at frequency bands associated with motion artifacts than either traditional high-pass filtering or principal component analysis filtering. Canonical correlation analysis filtering also minimized signal reduction at frequency bands expected to consist of true myoelectric signal. Conclusions: Canonical correlation analysis filtering appears to outperform a standard high-pass filter and principal component analysis filter in cleaning high-density EMG collected during fast walking or running.

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas

Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations. Here, we compared the reliabilities of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001 cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ∽1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was >80%. Using a template of electrode locations reduced the Brodmann area accuracy to ∽50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.

Recording mobile EEG in an outdoor environment reveals cognitive-motor interference dependent on movement complexity

Oftentimes we find ourselves in situations in which we need to perform concurrent motor and cognitive tasks like simple locomotion while being cognitively involved. In the present study, we investigated in how far cognitive and motor functioning interfere in an outdoor environment. Our participants performed an auditory oddball task while concurrently completing various motor tasks on the outside premises of our institute. Beside behavioural responses and subjective workload ratings, we also analysed electrophysiological data recorded with a 30-channel mobile EEG montage. We observed an increase of subjective workload and decrease of performance with increasing movement complexity. Accordingly, we also found a decrease in the parietal P3 amplitude as well as in frontal midline Theta power with higher motor load. These results indicate that an increased movement complexity imposes a higher workload to the cognitive system, which, in turn, effectively reduces the availability of cognitive resources for the cognitive task. Overall this experiment demonstrates the feasibility of transferring classical paradigms of cognitive research to real-world settings. The findings support the notion of shared resources for motor and cognitive functions by demonstrating distinct modulations of correlates of cognitive processes across different motor tasks.

Evaluating Mental Load During Realistic Driving Simulations by Means of Round the Ear Electrodes

Film based round the ear electrodes (cEEGrids) provide both, the accessibility of unobtrusive mobile EEG as well as a rapid EEG application in stationary settings when extended measurements are not possible. In a large-scale evaluation of driving abilities of older adults (N > 350) in a realistic driving simulation, we evaluated to what extent mental demands can be measured using cEEGrids in a completely unrestricted environment. For a first frequency-based analysis, the driving scenario was subdivided into different street segments with respect to their task loads (low, medium, high) that was a priori rated by an expert. Theta activity increased with task load but no change in Alpha power was found. Effects gained clarity after removing pink noise effects, that were potentially high in this data set due to motion artifacts. Theta fraction increased with task load and Alpha fraction decreased. We mapped this effect to specific street segments by applying a track-frequency analysis. Whilst participants drove with constant speed and without high steering wheel activity, Alpha was high and theta low. The reverse was the case in sections that required either high activity or increased attentional allocation to the driving context. When calculating mental demands for different street segments based on EEG, this measure is highly significant correlated with the experts' rating of task load. Deviances can be explained by specific features within the segments. Thus, modulations in spectral power of the EEG were validly reflected in the cEEGrids data. All findings were in line with the prominent literature in the field. The results clearly demonstrate the usability of this low-density EEG method for application in real-world settings where an increase in ecological validity might outweigh the loss of certain aspects of internal validity.

Combined head phantom and neural mass model validation of effective connectivity measures

OBJECTIVE

Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in a mobile setting. While many connectivity estimators are available, the efficacy of these measures has not been rigorously validated in real-world scenarios. The goal of this study was to quantify the accuracy of independent component analysis and multiple connectivity measures on ground-truth connections while exposed real-world volume conduction and head motion.

APPROACH

We collected high-density EEG from a phantom head with embedded antennae, using neural mass models to generate transiently interconnected signals. The head was mounted upon a motion platform that mimicked recorded human head motion at various walking speeds. We used cross-correlation and signal to noise ratio to determine how well independent component analysis recovered the original antenna signals. For connectivity measures, we computed the average and standard deviation across frequency of each estimated connectivity peak.

MAIN RESULTS

Independent component analysis recovered most antenna signals, as evidenced by cross-correlations primarily above 0.8, and maintained consistent signal to noise ratio values near 10 dB across walking speeds compared to scalp channel data, which had decreased signal to noise ratios of ~2 dB at fast walking speeds. The connectivity measures used were generally able to identify the true interconnections, but some measures were susceptible to spurious high-frequency connections inducing large standard deviations of ~10 Hz.

SIGNIFICANCE

Our results indicate that independent component analysis and some connectivity measures can be effective at recovering underlying connections among brain areas. These results highlight the utility of validating EEG processing techniques with a combination of complex signals, phantom head use, and realistic head motion.

Human electrocortical dynamics while stepping over obstacles

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3–13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

Novel gait training alters functional brain connectivity during walking in chronic stroke patients: a randomized controlled pilot trial

Background

A recent study has demonstrated that a turning-based treadmill program yields greater improvements in gait speed and temporal symmetry than regular treadmill training in chronic stroke patients. However, it remains unknown how this novel and challenging gait training shapes the cortico-cortical network and cortico-spinal network during walking in chronic stroke patients. The purpose of this study was to examine how a novel type of gait training, which is an unfamiliar but effective task for people with chronic stroke, enhances brain reorganization.

Methods

Subjects in the experimental and control groups received 30 min of turning-based treadmill training and regular treadmill training, respectively. Cortico-cortical connectivity and cortico-muscular connectivity during walking and gait performance were assessed before and after completing the 12-session training.

Results

Eighteen subjects (n = 9 per group) with a mean age of 52.5 ± 9.7 years and an overground walking speed of 0.61 ± 0.26 m/s consented and participated in this study. There were significant group by time interactions for gait speed, temporal gait symmetry, and cortico-cortical connectivity as well as cortico-muscular connectivity in walk-related frequency (24–40 Hz) over the frontal-central-parietal areas. Compared with the regular treadmill training, the turning-based treadmill training resulted in greater improvements in these measures. Moreover, the increases in cortico-cortical connectivity and cortico-muscular connectivity while walking were associated with improvements in temporal gait symmetry.

Conclusions

Our findings suggest this novel turning-based treadmill training is effective for enhancing brain functional reorganization underlying cortico-cortical and corticomuscular mechanisms and thus may result in gait improvement in people with chronic stroke.

Trial registration

ACTRN12617000190303. Registered 3 February 2017, retrospectively registered.

Removal of Gross Artifacts of Transcranial Alternating Current Stimulation in Simultaneous EEG Monitoring

Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. This paper presents two different algorithms for removing the gross tACS artifact from simultaneous EEG recordings. These give different trade-offs in removal performance, in the amount of data required, and in their suitability for closed loop systems. Superposition of Moving Averages and Adaptive Filtering techniques are investigated, with significant emphasis on verification. We present head phantom testing results for controlled analysis, together with on-person EEG recordings in the time domain, frequency domain, and Event Related Potential (ERP) domain. The results show that EEG during tACS can be recovered free of large scale stimulation artifacts. Previous studies have not quantified the performance of the tACS artifact removal procedures, instead focusing on the removal of second order artifacts such as respiration related oscillations. We focus on the unresolved challenge of removing the first order stimulation artifact, presented with a new multi-stage validation strategy.

Wearable EEG and beyond

The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of "wearable EEG" which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal "tattoo" type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

Development of a Modular Board for EEG Signal Acquisition

The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.