Anticipatory cardiac deceleration estimates cognitive performance in virtual reality beyond tonic heart period and heart period variability.

Anticipatory cardiac deceleration is the lengthening of heart period before an expected event. It appears to reflect preparation that supports rapid action. The current study sought to bolster anticipatory deceleration as a practical and unique estimator of performance efficiency. To this end, we examined relationships between deceleration and virtual reality performance under low and high time pressure. Importantly, we investigated whether deceleration separately estimates performance beyond basal heart period and basal high-frequency heart rate variability (other vagally influenced metrics related to cognition). Thirty participants completed an immersive virtual reality (VR) cognitive performance task across six longitudinal sessions. Anticipatory deceleration and basal heart period/heart period variability were quantified from electrocardiography collected during pre-task anticipatory countdowns and baseline periods, respectively. At the between-person level, we found that greater anticipatory declaration was related to superior accuracy and faster response times (RT). The relation between deceleration and accuracy was stronger under high relative to low time pressure, when good performance requires greater efficiency. Findings for heart period and heart period variability largely converge with the prior literature, but importantly, were statistically separate from deceleration effects on performance. Lastly, deceleration effects were detected using anticipatory periods that are more practical (shorter and more intermittent) than those typically employed. Taken together, findings suggest that anticipatory deceleration is a unique and practical correlate of cognitive-motor efficiency apart from heart period and heart period variability in virtual reality.

Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study.

Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4-7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.

A pilot study to understand the relationship between cortical arousals and leg movements during sleep.

Leg movements during sleep occur in patients with sleep pathology and healthy individuals. Some (but not all) leg movements during sleep are related to cortical arousals which occur without conscious awareness but have a significant effect of sleep fragmentation. Detecting leg movements during sleep that are associated with cortical arousals can provide unique insight into the nature and quality of sleep. In this study, a novel leg movement monitor that uses a unique capacitive displacement sensor and 6-axis inertial measurement unit, is used in conjunction with polysomnography to understand the relationship between leg movement and electroencephalogram (EEG) defined cortical arousals. In an approach that we call neuro-extremity analysis, directed connectivity metrics are used to interrogate causal linkages between EEG and leg movements measured by the leg movement sensors. The capacitive displacement measures were more closely related to EEG-defined cortical arousals than inertial measurements. Second, the neuro-extremity analysis reveals a temporally evolving connectivity pattern that is consistent with a model of cortical arousals in which brainstem dysfunction leads to near-instantaneous leg movements and a delayed, filtered signal to the cortex leading to the cortical arousal during sleep.

Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?

PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually required to detect McA-limiting large-scale, prospective studies on McA and their impact on health. Even with the use of EEG, reliably measuring McA can be difficult because of low inter-scorer reliability. Surrogate measures in place of EEG could provide easier and possibly more reliable measures of McA. These have usually involved measuring heart rate and arm movements. They have not provided a reliable measurement of McA in part because they cannot adequately detect short wake periods and periods of wake after sleep onset. Leg movements in sleep (LMS) offer an attractive alternative. LMS and cortical arousal, including McA, commonly occur together. Not all McA occur with LMS, but the most clinically significant ones may be those with LMS [1]. Conversely, most LMS do not occur with McA, but LMS vary considerably in their characteristics. Evaluating LMS characteristics may serve to identify the LMS associated with McA. The use of standard machine learning approaches seems appropriate for this particular task. This proof-of-concept pilot project aims to determine the feasibility of detecting McA from machine learning methods analyzing movement characteristics of the LMS. METHODS: This study uses a small but diverse group of subjects to provide a large variety of LMS and McA adequate for supervised machine learning. LMS measurements were obtained from a new advanced technology in the RestEaZe™ leg band that integrates gyroscope, accelerometer, and capacitance measurements. Eleven RestEaZe™ LMS features were selected for logistic regression analyses. RESULTS: With the optimum logit probability threshold selected, the system accurately detected 76% of the McA matching the accuracy of trained visual inter-scorer reliability (71-76%). The classifier provided a sensitivity of 76% and a specificity of 86% for the identification of the LMS with McA. The classifier identified regions in sleep with high versus low rates of LMS with McA, indicating possible areas of fragmented versus undisturbed restful sleep. CONCLUSION: These pilot data are encouraging as a preliminary proof-of-concept for using advanced machine learning analyses of LMS to identify sleep fragmented by McA.

Integrating end-user feedback in the concept stage of development of a novel sensor access system for environmental control.

PURPOSE: This article illustrates user-centred design of a novel sensor access system for environmental control in the concept stage of development. METHODS: Focus groups of individuals with disabilities and rehabilitation healthcare professionals were provided with video illustration of the technology and asked to provide quantitative and qualitative feedback through a semistructured interview process. Qualitative methods were employed to analyse transcribed comments to develop themes supporting ongoing development of the technology. RESULTS: Both end-user streams rated the original design features of the sensor access system (alternative interface to assistive technologies, having wireless capabilities and not requiring batteries) as having high potential value. Both groups identified a need for the future design of the sensor technology to be able to capture minimal/reduced movements for those with severe physical impairments. Themes included (1) the sensor technology could be individualized/customized to accommodate the user, (2) minimal positioning and set-up requirement and (3) technology that alleviated problems encountered with touch-based solutions. CONCLUSIONS: Inclusion of end-user feedback provided the research team with valuable information that supported the initial conceptualization of the design features of the technology and provided valuable data to support development of a new prototype that can capture more reduced/minimal movements. Implication for Rehabilitation User-centered design of assistive technology is essential to the development of technology that can meet the unique needs of those with the most severe physical impairments. New sensor technology may alleviate some of the access challenges faced by individuals with severe physical impairments. Collaboration between all key stakeholders (individuals with disabilities, rehabilitation professionals, researchers, and developers) is an essential component in the iterative assistive technology design process.

Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment.

INTRODUCTION: This paper explores the feasibility of using touchless textile sensors as an input to environmental control for individuals with upper-extremity mobility impairments. These sensors are capacitive textile sensors embedded into clothing and act as proximity sensors. METHODS: We present results from five individuals with spinal cord injury as they perform gestures that mimic an alphanumeric gesture set. The gestures are used for controlling appliances in a home setting. Our setup included a custom visualization that provides feedback to the individual on how the system is tracking the movement and the type of gesture being recognized. Our study included a two-stage session at a medical school with five subjects with upper extremity mobility impairment. RESULTS: The experimenting sessions derived binary gesture classification accuracies greater than 90% on average. The sessions also revealed intricate details in participant's motions, from which we draw two key insights on the design of the wearable sensor system. CONCLUSION: First, we provide evidence that personalization is a critical ingredient to the success of wearable sensing in this population group. The sensor hardware, the gesture set, and the underlying gesture recognition algorithm must be personalized to the individual's need and injury level. Secondly, we show that explicit feedback to the user is useful when the user is being trained on the system. Moreover, being able to see the end goal of controlling appliances using the system is a key motivation to properly learn gestures.