Effects of speed and direction of perturbation on electroencephalographic and balance responses.

The modulation of perturbation-evoked potential (PEP) N1 as a function of different biomechanical characteristics of perturbation has been investigated before. However, it remains unknown whether the PEP N1 modulation contributes to the shaping of the functional postural response. To improve this understanding, we examined the modulation of functional postural response in relation to the PEP N1 response in ten healthy young subjects during unpredictable perturbations to their upright stance-translations of the support surface in a forward or backward direction at two different amplitudes of constant speed. Using independent components from the fronto-central region, obtained from subject-specific head models created from the MRI, our results show that the latency of onset of the functional postural response after the PEP N1 response was faster for forward than backward perturbations at a constant speed but was not affected by the speed of perturbation. Further, our results reinforce some of the previous findings that suggested that the N1 peak amplitude and peak latency are both modulated by the speed of perturbation but not by the direction of the perturbation. Our results improve the understanding of the relation between characteristics of perturbation and the neurophysiology of reactive balance control and may have implications for the design of brain-machine interfaces for populations with a higher risk of falls.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

Significance: We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim: Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach: We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results: The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions: The advances presented here should serve to make fNIRS more accessible for BCI applications.

Assessment of Biomechanical Predictors of Occurrence of Low-Amplitude N1 Potentials Evoked by Naturally Occurring Postural Instabilities.

Naturally occurring postural instabilities that occur while standing and walking elicit specific cortical responses in the fronto-central regions (N1 potentials) followed by corrective balance responses to prevent falling. However, no framework could simultaneously track different biomechanical parameters preceding N1s, predict N1s, and assess their predictive power. Here, we propose a framework and show its utility by examining cortical activity (through electroencephalography [EEG]), ground reaction forces, and head acceleration in the anterior-posterior (AP) direction. Ten healthy young adults carried out a balance task of standing on a support surface with or without sway referencing in the AP direction, amplifying, or dampening natural body sway. Using independent components from the fronto-central cortical region obtained from subject-specific head models, we first robustly validated a prior approach on identifying low-amplitude N1 potentials before early signs of balance corrections. Then, a machine learning algorithm was used to evaluate different biomechanical parameters obtained before N1 potentials, to predict the occurrence of N1s. When different biomechanical parameters were directly compared, the time to boundary (TTB) was found to be the best predictor of the occurrence of upcoming low-amplitude N1 potentials during a balance task. Based on these findings, we confirm that the spatio-temporal characteristics of the center of pressure (COP) might serve as an essential parameter that can facilitate the early detection of postural instability in a balance task. Extending our framework to identify such biomarkers in dynamic situations like walking might improve the implementation of corrective balance responses through brain-machine-interfaces to reduce falls in the elderly.

A Roadmap Towards Standards for Neurally Controlled End Effectors.

The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.

Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs.

Fronto-Parietal Brain Areas Contribute to the Online Control of Posture during a Continuous Balance Task.

Neuroimaging studies have provided evidence for the involvement of frontal and parietal cortices in postural control. However, the specific role of these brain areas for postural control remains to be known. Here, we investigated the effects of disruptive transcranial magnetic stimulation (TMS) over supplementary motor areas (SMA) during challenging continuous balance task in healthy young adults. We hypothesized that a virtual lesion of SMA will alter activation within the brain network identified using electroencephalography (EEG) and impair performance of the postural task. Twenty healthy young adults received either continuous theta burst stimulation (cTBS) or sham stimulation over SMA followed by the performance of a continuous balance task with or without somatosensory input distortion created by sway-referencing the support surface. cTBS over SMA compared to sham stimulation showed a smaller increase in root mean square of center of pressure as the difficulty of continuous balance task increased suggestive of altered postural control mechanisms to find a stable solution under challenging sensory conditions. Consistent with earlier studies, we found sources of EEG activation within anterior cingulate (AC), cingulate gyrus (CG), bilateral posterior parietal regions (PPC) during the balance task. Importantly, cTBS over SMA compared to sham stimulation altered EEG power within the identified fronto-parietal regions. These findings suggest that the changes in activation within distant fronto-parietal brain areas following cTBS over SMA contributed to the altered postural behavior. Our study confirms a critical role of AC, CG, and both PPC regions in calibrating online postural responses during a challenging continuous balance task.

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground.

Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain's involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.

Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking.

Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users' gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a closed-loop BCI has not been investigated. This study aims to investigate the changes of cortical involvement in human treadmill walking with and without BCI control of a walking avatar. Source localization revealed significant differences in cortical network activity between walking with and without closed-loop BCI control. Our results showed sustained α/µ suppression in the Posterior Parietal Cortex and Inferior Parietal Lobe, indicating increases of cortical involvement during walking with BCI control. We also observed significant increased activity of the Anterior Cingulate Cortex (ACC) in the low frequency band suggesting the presence of a cortical network involved in error monitoring and motor learning. Additionally, the presence of low γ modulations in the ACC and Superior Temporal Gyrus may associate with increases of voluntary control of human gait. This work is a further step toward the development of a novel training paradigm for improving the efficacy of rehabilitation in a top-down approach.

Electrocortical correlates of human level-ground, slope, and stair walking.

This study investigated electrocortical dynamics of human walking across different unconstrained walking conditions (i.e., level ground (LW), ramp ascent (RA), and stair ascent (SA)). Non-invasive active-electrode scalp electroencephalography (EEG) signals were recorded and a systematic EEG processing method was implemented to reduce artifacts. Source localization combined with independent component analysis and k-means clustering revealed the involvement of four clusters in the brain during the walking tasks: Left and Right Occipital Lobe (LOL, ROL), Posterior Parietal Cortex (PPC), and Central Sensorimotor Cortex (SMC). Results showed that the changes of spectral power in the PPC and SMC clusters were associated with the level of motor task demands. Specifically, we observed α and ß suppression at the beginning of the gait cycle in both SA and RA walking (relative to LW) in the SMC. Additionally, we observed significant ß rebound (synchronization) at the initial swing phase of the gait cycle, which may be indicative of active cortical signaling involved in maintaining the current locomotor state. An increase of low γ band power in this cluster was also found in SA walking. In the PPC, the low γ band power increased with the level of task demands (from LW to RA and SA). Additionally, our results provide evidence that electrocortical amplitude modulations (relative to average gait cycle) are correlated with the level of difficulty in locomotion tasks. Specifically, the modulations in the PPC shifted to higher frequency bands when the subjects walked in RA and SA conditions. Moreover, low γ modulations in the central sensorimotor area were observed in the LW walking and shifted to lower frequency bands in RA and SA walking. These findings extend our understanding of cortical dynamics of human walking at different level of locomotion task demands and reinforces the growing body of literature supporting a shared-control paradigm between spinal and cortical networks during locomotion.

Multi-Trial Gait Adaptation of Healthy Individuals during Visual Kinematic Perturbations.

Optimizing rehabilitation strategies requires understanding the effects of contextual cues on adaptation learning. Prior studies have examined these effects on the specificity of split-belt walking adaptation, showing that contextual visual cues can be manipulated to modulate the magnitude, transfer, and washout of split-belt-induced learning in humans. Specifically, manipulating the availability of vision during training or testing phases of learning resulted in differences in adaptive mechanisms for temporal and spatial features of walking. However, multi-trial locomotor training has been rarely explored when using visual kinematic gait perturbations. In this study, we investigated multi-trial locomotor adaptation in ten healthy individuals while applying visual kinematic perturbations. Subjects were instructed to control a moving cursor, which represented the position of their heel, to follow a prescribed heel path profile displayed on a monitor. The perturbations were introduced by scaling all of the lower limb joint angles by a factor of 0.7 (i.e., a gain change), resulting in visual feedback errors between subjects' heel trajectories and the prescribed path profiles. Our findings suggest that, with practice, the subjects learned, albeit with different strategies, to reduce the tracking errors and showed faster response time in later trials. Moreover, the gait symmetry indices, in both the spatial and temporal domains, changed significantly during gait adaptation (P < 0.001). After-effects were present in the temporal gait symmetry index whens the visual perturbations were removed in the post-exposure period (P < 0.001), suggesting adaptation learning. These findings may have implications for developing novel gait rehabilitation interventions.

Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability.

Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.

Powered exoskeletons for bipedal locomotion after spinal cord injury.

OBJECTIVE: Powered exoskeletons promise to increase the quality of life of people with lower-body paralysis or weakened legs by assisting or restoring legged mobility while providing health benefits across multiple physiological systems. Here, a systematic review of the literature on powered exoskeletons addressed critical questions: What is the current evidence of clinical efficacy for lower-limb powered exoskeletons? What are the benefits and risks for individuals with spinal cord injury (SCI)? What are the levels of injury considered in such studies? What are their outcome measures? What are the opportunities for the next generation exoskeletons? APPROACH: A systematic search of online databases was performed to identify clinical trials and safety or efficacy studies with lower-limb powered exoskeletons for individuals with SCI. Twenty-two studies with eight powered exoskeletons thus selected, were analyzed based on the protocol design, subject demographics, study duration, and primary/secondary outcome measures for assessing exoskeleton's performance in SCI subjects. MAIN RESULTS: Findings show that the level of injury varies across studies, with T10 injuries being represented in 45.4% of the studies. A categorical breakdown of outcome measures revealed 63% of these measures were gait and ambulation related, followed by energy expenditure (16%), physiological improvements (13%), and usability and comfort (8%). Moreover, outcome measures varied across studies, and none had measures spanning every category, making comparisons difficult. SIGNIFICANCE: This review of the literature shows that a majority of current studies focus on thoracic level injury as well as there is an emphasis on ambulatory-related primary outcome measures. Future research should: 1) develop criteria for optimal selection and training of patients most likely to benefit from this technology, 2) design multimodal gait intention detection systems that engage and empower the user, 3) develop real-time monitoring and diagnostic capabilities, and 4) adopt comprehensive metrics for assessing safety, benefits, and usability.

A Closed-loop Brain Computer Interface to a Virtual Reality Avatar: Gait Adaptation to Visual Kinematic Perturbations.

The control of human bipedal locomotion is of great interest to the field of lower-body brain computer interfaces (BCIs) for rehabilitation of gait. While the feasibility of a closed-loop BCI system for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a virtual reality (BCI-VR) environment has yet to be demonstrated. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control the walking movements of a virtual avatar. Moreover, virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. These findings have implications for the development of BCI-VR systems for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI system.

Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences.

The brain response to conceptual art was studied with mobile electroencephalography (EEG) to examine the neural basis of aesthetic experiences. In contrast to most studies of perceptual phenomena, participants were moving and thinking freely as they viewed the exhibit The Boundary of Life is Quietly Crossed by Dario Robleto at the Menil Collection-Houston. The brain activity of over 400 subjects was recorded using dry-electrode and one reference gel-based EEG systems over a period of 3 months. Here, we report initial findings based on the reference system. EEG segments corresponding to each art piece were grouped into one of three classes (complex, moderate, and baseline) based on analysis of a digital image of each piece. Time, frequency, and wavelet features extracted from EEG were used to classify patterns associated with viewing art, and ranked based on their relevance for classification. The maximum classification accuracy was 55% (chance = 33%) with delta and gamma features the most relevant for classification. Functional analysis revealed a significant increase in connection strength in localized brain networks while subjects viewed the most aesthetically pleasing art compared to viewing a blank wall. The direction of signal flow showed early recruitment of broad posterior areas followed by focal anterior activation. Significant differences in the strength of connections were also observed across age and gender. This work provides evidence that EEG, deployed on freely behaving subjects, can detect selective signal flow in neural networks, identify significant differences between subject groups, and report with greater-than-chance accuracy the complexity of a subject's visual percept of aesthetically pleasing art. Our approach, which allows acquisition of neural activity "in action and context," could lead to understanding of how the brain integrates sensory input and its ongoing internal state to produce the phenomenon which we term aesthetic experience.