A Non-invasive Brain Computer Interface Decoder for Gait

Brain Computer Interface (BCI) systems enable control of machines and computers using signals extracted from the brain, such as data recorded using electroencephalography (EEG). Naturally, this technology is expected to help people with disabilities, such as lost speech or motor impairment, by providing an alternative approach to interact with the world. Being able to walk is one of the most fundamental human functions, and BCIs could help those with walking impairment by providing direct control of an exoskeleton directly from brain signals. The most crucial part of building such a system is the neural decoding–i.e., the specific algorithm that trans- lates neural signals into movement signals. Developing an effective neural decoding model does not only provide accurate control of the device, but could also open a new path towards understanding the neural representation of gait. A wide variety of algorithms have been proposed for neural decodings, such as linear regression, kalman filters, and artificial neural networks. However, there is a lack of rigorous comparisons of different decoding models and parameter choices. Furthermore, it is unclear how well each of these models will generalize to new data from either new environments or different subjects. This dissertation thesis aims to investigate those issues by: 1) Benchmarking the proposed models and understanding the representation of the brain during gait and 2) Study ways to generalize the model. In the first specific aim, we showed that neural networks not only performed better than conventional methods when trained within a specific walking environment, but resulted in models that were robust to external disturbances such as channel distortion. In the second aim, we showed intra- subject decoding works in all the combinations (e.g., inter-subject decoding of different terrains, level ground walking only, treadmill walking, etc.), but inter- subject decoding only works for electromyography (EMG) to kinematics decoding. To deal with this problem, several methods were used to improve inter- subject decoding. Of these methods, transfer learning achieved the most promising results. The work in this dissertation contributes to a greater understanding of the decoding models and their performance/generalizability on non-invasive gait decoding

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 β suppression at the beginning of the gait cycle in both SA and RA walking (relative to LW) in the SMC. Additionally, we observed significant β 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

Wogonin Attenuates Ovalbumin Antigen-Induced Neutrophilic Airway Inflammation by Inhibiting Th17 Differentiation

Allergic airway inflammation is generally considered to be a Th2-type immune response. Recent studies, however, have demonstrated that Th17-type immune responses also play important roles in this process, particularly in the pathogenesis of neutrophilic airway inflammation, a hallmark of severe asthma. We scrutinized several Kampo extracts that reportedly exhibit anti-inflammatory activity by using in vitro differentiation system of human and mouse naïve T cells. We found that hange-shashin-to (HST) and oren-gedoku-to (OGT) possess inhibitory activity for Th17 responses in vitro. Indeed, wogonin and berberine, major components common to HST and OGT, exhibit Th17-inhibitory activities in both murine and human systems in vitro. We therefore evaluated whether wogonin suppresses OVA-induced neutrophilic airway inflammation in OVA TCR-transgenic DO11.10 mice. Consequently, oral administration of wogonin significantly improved OVA-induced neutrophilic airway inflammation. Wogonin suppressed the differentiation of naïve T cells to Th17 cells, while showing no effects on activated Th17 cells

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

Full body mobile brain-body imaging data (EEG, EMG, and kinematics) during unconstrained locomotion on stairs, ramps, and level ground

This dataset contains full brain/body imaging data from ten able-bodied subjects during locomotion on an experimental course containing stairs, ramps, and level ground. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle cites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. This research was partly supported by NSF award IIS-1302339 and an NIH F99 Predoctoral Fellowship (NS105210) to Justin Brantley

Electrocortical correlates of human level-ground, slope, and stair walking - Fig 1

<p>A) Experimental setup in this study. Each subject was instrumented with EEG, EMG, and IMU sensors and a trigger system. B) Illustration for gait course setup which was designed to provide five steady locomotion modes (level ground walking, stair descent, stair ascent, ramp descent, and ramp ascent). C) Experimental protocol. The subjects began walking, descended the ramp, transitioned to level ground walking, ascended the staircase (8 steps, step height: ~13.3 cm), and came to rest at the end of the stair platform (forward path). Ambulation back to the starting point (backward path) constituted one complete test trial.</p

Clusters of dipolar sources fit to independent components for all subjects across all trials, which includes all walking conditions (LW, RA, and SA).

<p>Brodmann areas are the regions found within ±5mm search range of cluster centroids. ^a These clusters did not cover a majority of the subjects, and where excluded from further analysis.</p

Flow-chart indicating EEG data-processing pipeline.

<p>Flow-chart indicating EEG data-processing pipeline.</p

The changes of time-frequency spectrogram across different walking conditions.

<p>A) Topographical scalp projections of ICs comprising in the Posterior Parietal Cortex and the Central Sensorimotor clusters. B) and C) show power changes (relative to level-ground walking periods) in the ramp ascent (RA) and the stair ascent (SA) walking conditions, respectively. Displayed are only significant power changes (p < 0.05). The blue color indicates significant power decrease (ERD) and the brown color indicates power increase (ERS).</p