Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion

Phase coherence-A time-localized approach to studying interactions

Coherence measures the similarity of progression of phases between oscillations or waves. When applied to multi-scale, nonstationary dynamics with time-varying amplitudes and frequencies, high values of coherence provide a useful indication of interactions, which might otherwise go unnoticed. However, the choice of analyzing coherence based on phases and amplitudes (amplitude-weighted phase coherence) vs only phases (phase coherence) has long been seen as arbitrary. Here, we review the concept of coherence and focus on time-localized methods of analysis, considering both phase coherence and amplitude-weighted phase coherence. We discuss the importance of using time-localized analysis and illustrate the methods and their practicalities on both numerically modeled and real time-series. The results show that phase coherence is more robust than amplitude-weighted phase coherence to both noise perturbations and movement artifacts. The results also have wider implications for the analysis of real data and the interpretation of physical systems.

Dopamine improves defective cortical and muscular connectivity during bilateral control of gait in Parkinson's disease

Parkinson's Disease (PD)-typical declines in gait coordination are possibly explained by weakness in bilateral cortical and muscular connectivity. Here, we seek to determine whether this weakness and consequent decline in gait coordination is affected by dopamine levels. To this end, we compare cortico-cortical, cortico-muscular, and intermuscular connectivity and gait outcomes between body sides in people with PD under ON and OFF medication states, and in older adults. In our study, participants walked back and forth along a 12 m corridor. Gait events (heel strikes and toe-offs) and electrical cortical and muscular activities were measured and used to compute cortico-cortical, cortico-muscular, and intermuscular connectivity (i.e., coherences in the alpha, beta, and gamma bands), as well as features characterizing gait performance (e.g., the step-timing coordination, length, and speed). We observe that people with PD, mainly during the OFF medication, walk with reduced step-timing coordination. Additionally, our results suggest that dopamine intake in PD increases the overall cortico-muscular connectivity during the stance and swing phases of gait. We thus conclude that dopamine corrects defective feedback caused by impaired sensory-information processing and sensory-motor integration, thus increasing cortico-muscular coherences in the alpha bands and improving gait.

Resting EEG correlates of neurodevelopment in a socioeconomically and linguistically diverse sample of toddlers: Wave 1 of the Kia Tīmata Pai best start New Zealand study

Development of communication and self-regulation skills is fundamental to psychosocial maturation in childhood. The Kia Tīmata Pai Best Start (KTP) longitudinal study aims to promote these skills through interventions delivered at early childcare centers across New Zealand. In addition to evaluating effects of the interventions on behavioral and cognitive outcomes, the study utilizes electroencephalography (EEG) to characterize cortical development in a subsample of participating children. Here, we present results of the baseline resting EEG assessment with 193 children aged 15 to 33 months. We identified EEG correlates of individual differences in demographics, communication abilities, and temperament. We obtained communication and behavior ratings from multiple informants, and we applied contemporary analytic methods to the EEG data. Periodic spectral power adjusted for aperiodic activity was most closely associated with demographic, language, and behavioral measures. As in previous studies, gamma power was positively associated with verbal language. Alpha power was positively associated with effortful control. Nonverbal and verbal language measures showed distinct associations with EEG indices, as did the three temperament domains. Our results identified a number of candidate EEG measurements for use as longitudinal markers of optimal cortical development and response to interventions in the KTP cohort.

EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation

The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.

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.

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.

Immersive media experience: a survey of existing methods and tools for human influential factors assessment

Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

Dopaminergic medication reduces interhemispheric hyper-synchronization in Parkinson's disease

INTRODUCTION

We previously reported on interhemispheric cortical hyper synchronization in PD. The aim of the present study was to address the hypothesis that increased interhemispheric cortical synchronization in PD is related to dopamine deficiency and is correlated with motor function.

METHODS

We studied participants with PD and characterized cortical synchronization with reference to brain regions. Electroencephalography (EEG) was recorded from 20 participants with PD while OFF and ON their dopaminergic medications (two separate visits), during quiet standing and straight-line walking. Cortical interactions in the theta, alpha, beta, and gamma brain wave frequency bands were evaluated using interhemispheric phase synchronization (inter-PS).

RESULTS

Inter-PS values were found to be significantly higher during the OFF state as compared to the ON state in standing and walking trials for theta, alpha and beta bands. In addition, inter-PS reduction from OFF to ON was associated with mobility improvement evaluated by the Timed Up and Go test, and with daily levodopa equivalent dose across individuals. Higher differences in inter-PS values between OFF and ON states were evident mainly in the occipital-parietal cortex.

CONCLUSIONS

Persons with PD have increased inter-PS during the OFF state compared to their ON state, and this increase in inter-PS is associated with the clinical improvement between OFF and ON. We speculate that these findings, together with previous evidence of higher inter-PS in PD as compared to healthy older adults, reflect neuronal processes consequential to asymmetric subcortical dopamine deficiency.

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

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

Discussion of Research Priorities for Gait Disorders in Parkinson's Disease

Gait and balance abnormalities develop commonly in Parkinson's disease and are among the motor symptoms most disabling and refractory to dopaminergic or other treatments, including deep brain stimulation. Efforts to develop effective therapies are challenged by limited understanding of these complex disorders. There is a major need for novel and appropriately targeted research to expedite progress in this area. The Scientific Issues Committee of the International Parkinson and Movement Disorder Society has charged a panel of experts in the field to consider the current knowledge gaps and determine the research routes with highest potential to generate groundbreaking data. © 2021 International Parkinson and Movement Disorder Society.

Connectivity of EEG synchronization networks increases for Parkinson's disease patients with freezing of gait

Freezing of gait (FoG), a paroxysmal gait disturbance commonly experienced by patients with Parkinson's disease (PD), is characterized by sudden episodes of inability to generate effective forward stepping. Recent studies have shown an increase in beta frequency of local-field potentials in the basal-ganglia during FoG, however, comprehensive research on the synchronization between different brain locations and frequency bands in PD patients is scarce. Here, by developing tools based on network science and non-linear dynamics, we analyze synchronization networks of electroencephalography (EEG) brain waves of three PD patient groups with different FoG severity. We find higher EEG amplitude synchronization (stronger network links) between different brain locations as PD and FoG severity increase. These results are consistent across frequency bands (theta, alpha, beta, gamma) and independent of the specific motor task (walking, still standing, hand tapping) suggesting that an increase in severity of PD and FoG is associated with stronger EEG networks over a broad range of brain frequencies. This observation of a direct relationship of PD/FoG severity with overall EEG synchronization together with our proposed EEG synchronization network approach may be used for evaluating FoG propensity and help to gain further insight into PD and the pathophysiology leading to FoG.

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 pilot study assessing reliability and age-related differences in corticomuscular and intramuscular coherence in ankle dorsiflexors during walking

Corticomuscular (CMC) and intramuscular (intraMC) coherence represent measures of corticospinal interaction. Both CMC and intraMC can be assessed during human locomotion tasks, for example, while walking. Corticospinal control of gait can deteriorate during the aging process and CMC and intraMC may represent an important monitoring means. However, it is unclear whether such assessments represent a reliable tool when performed during walking in an ecologically valid scenario and whether age‐related differences may occur. Wireless surface electroencephalography and electromyography were employed in a pilot study with young and old adults during overground walking in two separate sessions. CMC and intraMC analyses were performed in the gathered beta and lower gamma frequencies (i.e., 13–40 Hz). Significant log‐transformed coherence area was tested for intersessions test–retest reliability by determining intraclass correlation coefficient (ICC), yielding to low reliability in CMC in both younger and older adults. intraMC exclusively showed low reliability in the older adults, whereas intraMC in the younger adults revealed similar values as previously reported: test–retest reliability [ICC (95% CI): 0.44 (−0.23, 0.87); SEM: 0.46; MDC: 1.28; MDC%: 103; Hedge's g (95% CI): 0.54 (−0.13, 1.57)]. Significant differences between the age groups were observed in intraMC by either comparing the two groups with the first test [Hedge's g (95% CI): 1.55 (0.85, 2.15); p‐value: .006] or with the retest data [Hedge's g (95% CI): 2.24 (0.73, 3.70); p‐value: .005]. Notwithstanding the small sample size investigated, intraMC seems a moderately reliable assessment in younger adults. The further development and use of this measure in practical settings to infer corticospinal interaction in human locomotion in clinical practice is warranted and should help to refine the analysis. This necessitates involving larger sample sizes as well as including a wider number of lower limb muscles. Moreover, further research seems warranted by the observed differences in modulation mechanisms of corticospinal control of gait as ascertained by intraMC between the age groups.

Wireless, Artefact Aware Impedance Sensor Node for Continuous Bio-Impedance Monitoring

Body bio-impedance is a unique parameter to monitor changes in body composition non-invasively. Continuous measurement of bio-impedance can track changes in body fluid content and cell mass and has widespread applications for physiological monitoring. State-of-the-art implementation of bio-impedance sensor devices is still limited for continuous use, in part, due to artefacts arising at the skin-electrode (SE) interface. Artefacts at the SE interface may arise due to various factors such as motion, applied pressure on the electrode surface, changes in ambient conditions or gradual drying of electrodes. This paper presents a novel bio-impedance sensor node that includes an artefact aware method for bio-impedance measurement. The sensor node enables autonomous and continuous measurement of bio-impedance and SE contact impedance at ten frequencies between 10 kHz to 100 kHz to detect artefacts at the SE interface. Experimental evaluation with SE contact impedance models using passive 2R1C electronic circuits and also with non-invasive in vivo measurements of SE contact impedance demonstrated high accuracy (with maximum error less than 1.5%) and precision of 0.6 Ω. The ability to detect artefacts caused by motion, vertically applied pressure and skin temperature changes was analysed in proof of concept experiments. Low power sensor node design achieved with 50mW in active mode and only 143 μW in sleep mode estimated a battery life of 90 days with a 250 mAh battery and duty-cycling impedance measurements every 60 seconds. Our method for artefact aware bio-impedance sensing is a step towards autonomous and unobtrusive continuous bio-impedance measurement for health monitoring at-home or in clinical environments.

A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings

Brain activity during natural walking outdoors can be captured using mobile electroencephalography (EEG). However, EEG recorded during gait is confounded with artifacts from various sources, possibly obstructing the interpretation of brain activity patterns. Currently, there is no consensus on how the amount of artifact present in these recordings should be quantified, or is there a systematic description of gait artifact properties. In the current study, we expand several features into a seven-dimensional footprint of gait-related artifacts, combining features of time, time-frequency, spatial, and source domains. EEG of N = 26 participants was recorded while standing and walking outdoors. Footprints of gait-related artifacts before and after two different artifact attenuation strategies (after artifact subspace reconstruction (ASR) and after subsequent independent component analysis [ICA]) were systematically different. We also evaluated topographies, morphologies, and signal-to-noise ratios (SNR) of button-press event-related potentials (ERP) before and after artifact handling, to confirm gait-artifact reduction specificity. Morphologies and SNR remained unchanged after artifact attenuation, whereas topographies improved in quality. Our results show that the footprint can provide a detailed assessment of gait-related artifacts and can be used to estimate the sensitivity of different artifact reduction strategies. Moreover, the analysis of button-press ERPs demonstrated its specificity, as processing did not only reduce gait-related artifacts but ERPs of interest remained largely unchanged. We conclude that the proposed footprint is well suited to characterize individual differences in gait-related artifact extent. In the future, it could be used to compare and optimize recording setups and processing pipelines comprehensively.

Freezing of gait: understanding the complexity of an enigmatic phenomenon

Diverse but complementary methodologies are required to uncover the complex determinants and pathophysiology of freezing of gait. To develop future therapeutic avenues, we need a deeper understanding of the disseminated functional-anatomic network and its temporally associated dynamic processes. In this targeted review, we will summarize the latest advances across multiple methodological domains including clinical phenomenology, neurogenetics, multimodal neuroimaging, neurophysiology, and neuromodulation. We found that (i) locomotor network vulnerability is established by structural damage, e.g. from neurodegeneration possibly as result from genetic variability, or to variable degree from brain lesions. This leads to an enhanced network susceptibility, where (ii) modulators can both increase or decrease the threshold to express freezing of gait. Consequent to a threshold decrease, (iii) neuronal integration failure of a multilevel brain network will occur and affect one or numerous nodes and projections of the multilevel network. Finally, (iv) an ultimate pathway might encounter failure of effective motor output and give rise to freezing of gait as clinical endpoint. In conclusion, we derive key questions from this review that challenge this pathophysiological view. We suggest that future research on these questions should lead to improved pathophysiological insight and enhanced therapeutic strategies.

EEG Movement Artifact Suppression in Interactive Virtual Reality

The integration of electroencephalogram (EEG) sensors into virtual reality (VR) headsets can provide the capability of tracking the user's cognitive state and eventually be used to increase the sense of immersion. Recent developments in wireless, room-scale VR tracking allow users to move freely in the physical and virtual spaces. Such motion can create significant movement artifacts in EEG sensors mounted to the VR headset. This study explores the removal of EEG movement artifacts caused by repetitive, stereotyped movements during an interactive VR task.

Quantitative Analysis of EEG Power Spectrum and EMG Median Power Frequency Changes after Continuous Passive Motion Mirror Therapy System

Robotic mirror therapy (MT), which allows movement of the affected limb, is proposed as a more effective method than conventional MT (CMT). To improve the rehabilitation effectiveness of post-stroke patients, we developed a sensory stimulation-based continuous passive motion (CPM)-MT system with two different operating protocols, that is, asynchronous and synchronous modes. To evaluate their effectiveness, we measured brain activation through relative and absolute power spectral density (PSD) changes of electroencephalogram (EEG) mu rhythm in three cases with CMT and CPM-MT with asynchronous and synchronous modes. We also monitored changes in muscle fatigue, which is one of the negative effects of the CPM device, based on median power frequency (MPF) and root mean square (RMS). Relative PSD was most suppressed when subjects used the CPM-MT system under synchronous control: 22.11%, 15.31%, and 16.48% on Cz, C3, and C4, respectively. The absolute average changes in MPF and RMS were 1.59% and 9.78%, respectively, with CPM-MT. Synchronous mode CPM-MT is the most effective method for brain activation, and muscle fatigue caused by the CPM-MT system was negligible. This study suggests the more effective combination rehabilitation system for MT by utilizing CPM and magnetic-based MT task to add action execution and sensory stimulation compared with CMT.

Corticospinal Control of Human Locomotion as a New Determinant of Age-Related Sarcopenia: An Exploratory Study

Sarcopenia is a muscle disease listed within the ICD-10 classification. Several operational definitions have been created for sarcopenia screening; however, an international consensus is lacking. The Centers for Disease Control and Prevention have recently recognized that sarcopenia detection requires improved diagnosis and screening measures. Mounting evidence hints towards changes in the corticospinal communication system where corticomuscular coherence (CMC) reflects an effective mechanism of corticospinal interaction. CMC can be assessed during locomotion by means of simultaneously measuring Electroencephalography (EEG) and Electromyography (EMG). The aim of this study was to perform sarcopenia screening in community-dwelling older adults and explore the possibility of using CMC assessed during gait to discriminate between sarcopenic and non-sarcopenic older adults. Receiver Operating Characteristic (ROC) curves showed high sensitivity, precision and accuracy of CMC assessed from EEG Cz sensor and EMG sensors located over Musculus Vastus Medialis [Cz-VM; AUC (95.0%CI): 0.98 (0.92-1.04), sensitivity: 1.00, 1-specificity: 0.89, p < 0.001] and with Musculus Biceps Femoris [Cz-BF; AUC (95.0%CI): 0.86 (0.68-1.03), sensitivity: 1.00, 1-specificity: 0.70, p < 0.001]. These muscles showed significant differences with large magnitude of effect between sarcopenic and non-sarcopenic older adults [Hedge's g (95.0%CI): 2.2 (1.3-3.1), p = 0.005 and Hedge's g (95.0%CI): 1.5 (0.7-2.2), p = 0.010; respectively]. The novelty of this exploratory investigation is the hint toward a novel possible determinant of age-related sarcopenia, derived from corticospinal control of locomotion and shown by the observed large differences in CMC when sarcopenic and non-sarcopenic older adults are compared. This, in turn, might represent in future a potential treatment target to counteract sarcopenia as well as a parameter to monitor the progression of the disease and/or the potential recovery following other treatment interventions.

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.

Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.

Characterization and real-time removal of motion artifacts from EEG signals

OBJECTIVE

Accurate implementation of real-time non-invasive brain-machine/computer interfaces (BMI/BCI) requires handling physiological and nonphysiological artifacts associated with the measurement modalities. For example, scalp electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible.

APPROACH

We present an adaptive de-noising framework with robustness properties, using a Volterra based non-linear mapping to characterize and handle the motion artifact contamination in EEG measurements. We asked healthy able-bodied subjects to walk on a treadmill at gait speeds of 1-to-4 mph, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system. We also placed inertial measurement unit (IMU) sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait.

MAIN RESULTS

We discuss in detail the characteristics of the motion artifacts and propose a real-time compatible solution to filter them. We report the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-related spectral perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies.

SIGNIFICANCE

The real-time compatibility and generalizability of our adaptive filtering framework allows for the effective use of non-invasive BMI/BCI systems and greatly expands the implementation type and application domains to other types of problems where signal denoising is desirable. Combined with our previous efforts of filtering ocular artifacts, the presented technique allows for a comprehensive adaptive filtering framework to increase the EEG signal to noise ratio (SNR). We believe the implementation will benefit all non-invasive neural measurement modalities, including studies discussing neural correlates of movement and other internal states, not necessarily of BMI focus.

Coupling Between Leg Muscle Activation and EEG During Normal Walking, Intentional Stops, and Freezing of Gait in Parkinson's Disease

In this paper, we apply novel techniques for characterizing leg muscle activation patterns via electromyograms (EMGs) and for relating them to changes in electroencephalogram (EEG) activity during gait experiments. Specifically, we investigate changes of leg-muscle EMG amplitudes and EMG frequencies during walking, intentional stops, and unintended freezing-of-gait (FOG) episodes. FOG is a frequent paroxysmal gait disturbance occurring in many patients suffering from Parkinson's disease (PD). We find that EMG amplitudes and frequencies do not change significantly during FOG episodes with respect to walking, while drastic changes occur during intentional stops. Phase synchronization between EMG signals is most pronounced during walking in controls and reduced in PD patients. By analyzing cross-correlations between changes in EMG patterns and brain-wave amplitudes (from EEGs), we find an increase in EEG-EMG coupling at the beginning of stop and FOG episodes. Our results may help to better understand the enigmatic pathophysiology of FOG, to differentiate between FOG events and other gait disturbances, and ultimately to improve diagnostic procedures for patients suffering from PD.

A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.

Cortical Correlates of Locomotor Muscle Synergy Activation in Humans: An Electroencephalographic Decoding Study

Muscular control during walking is believed to be simplified by the coactivation of muscles called muscle synergies. Although significant corticomuscular connectivity during walking has been reported, the level at which the cortical activity is involved in muscle activity (muscle synergy or individual muscle level) remains unclear. Here we examined cortical correlates of muscle activation during walking by brain decoding of activation of muscle synergies and individual muscles from electroencephalographic signals. We demonstrated that the activation of locomotor muscle synergies was decoded from slow cortical waves. In addition, the decoding accuracy for muscle synergies was greater than that for individual muscles and the decoding of individual muscle activation was based on muscle-synergy-related cortical information. These results indicate the cortical correlates of locomotor muscle synergy activation. These findings expand our understanding of the relationships between brain and locomotor muscle synergies and could accelerate the development of effective brain-machine interfaces for walking rehabilitation.

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.

Monitoring Pilot's Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions

Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the "brain at work" in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations.

Probing Corticospinal Control During Different Locomotor Tasks Using Detailed Time-Frequency Analysis of Electromyograms

Locomotion relies on the fine-tuned coordination of different muscles which are controlled by particular neural circuits. Depending on the attendant conditions, walking patterns must be modified to optimally meet the demands of the task. Assessing neuromuscular control during dynamic conditions is methodologically highly challenging and prone to artifacts. Here we aim at assessing corticospinal involvement during different locomotor tasks using non-invasive surface electromyography. Activity in tibialis anterior (TA) and gastrocnemius medialis (GM) muscles was monitored by electromyograms (EMGs) in 27 healthy volunteers (11 female) during regular walking, walking while engaged in simultaneous cognitive dual tasks, walking with partial visual restriction, and skilled, targeted locomotion. Whereas EMG intensity of the TA and GM was considerably altered while walking with partial visual restriction and during targeted locomotion, dual-task walking induced only minor changes in total EMG intensity compared to regular walking. Targeted walking resulted in enhanced EMG intensity of GM in the frequency range associated with Piper rhythm synchronies. Likewise, targeted walking induced enhanced EMG intensity of TA at the Piper rhythm frequency around heelstrike, but not during the swing phase. Our findings indicate task- and phase-dependent modulations of neuromuscular control in distal leg muscles during various locomotor conditions in healthy subjects. Enhanced EMG intensity in the Piper rhythm frequency during targeted walking points toward enforced corticospinal drive during challenging locomotor tasks. These findings indicate that comprehensive time-frequency EMG analysis is able to gauge cortical involvement during different movement programs in a non-invasive manner and might be used as complementary diagnostic tool to assess baseline integrity of the corticospinal tract and to monitor changes in corticospinal drive as induced by neurorehabilitation interventions or during disease progression.

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

Aim: The aim of this work was to study the neurophysiological effect of repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) in 8 patients with major depression disorder (MDD) and 10 patients with bipolar disorder (BP), considering separately responders and non-responders to rTMS therapy in each of both groups. Methods: The Higuchi's Fractal Dimension (FD) was analyzed from 64-channels EEG signals in five physiological frequency bands and every channel separately. Changes of FD were analyzed before and after 1st, 10th, and 20th session of rTMS. Results: Some differences in response to the rTMS therapy was found across individual groups. In MDD responders, FD decreased in all bands after longer stimulation (20th session). Whereas, in BP non-responders, FD decreased after 1st session in all bands as well as after 10th session in lower frequencies (delta and theta). In MDD non-responders and BP responders FD increased at the beginning of the therapy (1st and 10th session, respectively), but the final FD value did not changed in comparison to the initial FD value, except the FD decrease for theta band in BP responders. Comparison between groups showed a higher FD in MDD responders than in MDD non-responders in every band before as well as after stimulation. In contrast to MDD patients, FD was lower in BP responders than in BP non-responders in higher frequency bands (alpha, beta, and gamma) in both conditions as well as in lower frequency bands (delta and theta) after stimulation. Comparing both groups of responders, FD was lower in MDD than in BP in every band, except alpha. In case of non-responders, FD was higher in BP than in MDD in all bands in both conditions. Conclusion: The results showed that FD may be useful marker for evaluation of the rTMS effectiveness and the therapy progress as well as for group differentiation between MDD and BP or between responders and non-responders. The changes of FD under the influence of rTMS allow to unambiguously conclude whether the effect of stimulation is positive or negative as well as allow to evaluate an optimal time of rTMS.