Objective: While non-invasive, cuffless blood pressure (BP) measurement has demonstrated relevancy in controlled environments, ambulatory measurement is important for hypertension diagnosis and control. We present both in-lab and ambulatory BP estimation results from a diverse cohort of participants. Methods: Participants (N=1125, aged 21-85, 49.2% female, multiple hypertensive categories) had BP measured in-lab over a 24-hour period with a subset also receiving ambulatory measurements. Radial tonometry, photoplethysmography (PPG), electrocardiography (ECG), and accelerometry signals were collected simultaneously with auscultatory or oscillometric references for systolic (SBP) and diastolic blood pressure (DBP). Predictive models to estimate BP using a variety of sensor-based feature groups were evaluated against challenging baselines. Results: Despite limited availability, tonometry-derived features showed superior performance compared to other feature groups and baselines, yieldingprediction errors of 0.32$\pm$9.8 mmHg SBP and 0.54$\pm$7.7 mmHg DBP in-lab, and 0.86$\pm$8.7 mmHg SBP and 0.75$\pm$5.9 mmHg DBP for 24-hour averages. SBP error standard deviation (SD) was reduced in normotensive (in-lab: 8.1 mmHg, 24-hr: 7.2 mmHg) and younger (in-lab: 7.8 mmHg, 24-hr: 6.7 mmHg) subpopulations. SBP SD was further reduced 15–20% when constrained to the calibration posture alone. Conclusion: Performance for normotensive and younger participants was superior to the general population across all feature groups. Reference type, posture relative to calibration, and controlled vs. ambulatory setting all impacted BP errors. Significance: Results highlight the need for demographically diverse populations and challenging evaluation settings for BP estimation studies. We present the first public dataset of ambulatory tonometry and cuffless BP over a 24-hour period to aid in future cardiovascular research.

Brain–computer interfaces (BCIs) benefit greatly from performance feedback, but current systems lack automatic, task-independent feedback. Cortical responses elicited from user error have the potential to serve as state-based feedback to BCI decoders. To gain a better understanding of local error potentials, we investigate responsive cortical power underlying error-related potentials (ErrPs) from the human cortex during a one-dimensional center-out BCI task, tracking the topography of high-gamma (70–100 Hz) band power (HBP) specific to BCI error. We measured electrocorticography (ECoG) in three human subjects during dynamic, continuous control over BCI cursor velocity. Subjects used motor imagery and rest to move the cursor toward and subsequently dwell within a target region. We then identified and labeled epochs where the BCI decoder incorrectly moved the cursor in the direction opposite of the subject’s expectations (i.e., BCI error). We found increased HBP in various cortical areas 100–500 ms following BCI error with respect to epochs of correct, intended control. Significant responses were noted in primary somatosensory, motor, premotor, and parietal areas and generally regardless of whether the subject was using motor imagery or rest to move the cursor toward the target. Parts of somatosensory, temporal, and parietal areas exclusively had increased HBP when subjects were using motor imagery. In contrast, only part of the parietal cortex near the angular gyrus exclusively had an increase in HBP during rest. This investigation is, to our knowledge, the first to explore cortical fields changes in the context of continuous control in ECoG BCI. We present topographical changes in HBP characteristic specific to the generation of error. By focusing on continuous control, instead of on discrete control for simple selection, we investigate a more naturalistic setting and provide high ecological validity for characterizing error potentials. Such potentials could be considered as design elements for co-adaptive BCIs in the future as task-independent feedback to the decoder, allowing for more robust and individualized BCIs.

OBJECTIVE:The activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks. METHODS:Three neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70-230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording. RESULTS:In all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3-6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05). CONCLUSIONS:HG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

According to one embodiment of the present disclosure, a computing device is provided, comprising a processor configured to receive an input. The input includes a first pulse pressure signal obtained using a wearable tonometer affixed to a body of a user. The processor is further configured to apply a transfer function to the first pulse pressure signal, wherein the transfer function converts the first pulse pressure signal into a transformed pulse pressure signal. The transformed pulse pressure signal simulates a second pulse pressure signal of a handheld tonometer concurrently applied to the body of the user.

Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain’s motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.

Synchronized phase estimates between oscillating neuronal signals at the macroscale level reflect coordinated activities between neuronal assemblies. Recent electrophysiological evidence suggests the presence of significant spontaneous phase synchrony within the resting state. The purpose of this study was to investigate phase synchrony, including directional interactions, in resting state subdural electrocorticographic recordings to better characterize patterns of regional phase interactions across the lateral cortical surface during the resting state. We estimated spontaneous phase locking value (PLV) as a measure of functional connectivity, and phase slope index (PSI) as a measure of pseudo-causal phase interactions, across a broad range of canonical frequency bands and the modulation of the amplitude envelope of high gamma (amHG), a band that is believed to best reflect the physiological processes giving rise to the functional magnetic resonance imaging BOLD signal. Long-distance interactions had higher PLVs in slower frequencies (≤theta) than in higher ones (≥beta) with amHG behaving more like slow frequencies, and a general trend of increasing frequency band of significant PLVs when moving across the lateral surface along an anterior-posterior axis. Moreover, there was a strong trend of frontal-to-parietal directional phase synchronization, measured by PSI across multiple frequencies. These findings, which are likely indicative of coordinated and structured spontaneous cortical interactions, are important in the study of time scales and directional nature of resting state functional connectivity, and may ultimately contribute to a better understanding of how spontaneous synchrony is linked to variation in regional architecture across the lateral cortical surface.

Objective: The purpose of this study is to determine the relationship between cortical electrophysiological (CE) signals recorded from the surface of the brain (subdural electrocorticography, or ECoG) and signals recorded extracranially from the subgaleal (SG) space.Methods: We simultaneously recorded several hours of continuous ECoG and SG signals from 3 human pediatric subjects, and compared power spectra of signals between a differential SG montage and several differential ECoG montages to determine the nature of the transfer function between them.Results: We demonstrate the presence of CE signals in the SG montage in the high-gamma range (HG, 70-110 Hz), and the transfer function between 70 and 110 Hz is best characterized as a linear function of frequency. We also test an alternative transfer function, i.e. a single pole filter, to test the hypothesis of frequency dependent attenuation in that range, but find this model to be inferior to the linear model.Conclusions: Our findings indicate that SG electrodes are capable of recording HG signals without frequency distortion compared with ECoG electrodes.Significance: HG signals could be recorded minimally invasively from outside the skull, which could be important for clinical care or brain-computer interface applications. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A motor cortex-based brain-computer interface (BCI) creates a novel real world output directly from cortical activity. Use of a BCI has been demonstrated to be a learned skill that involves recruitment of neural populations that are directly linked to BCI control as well as those that are not. The nature of interactions between these populations, however, remains largely unknown. Here, we employed a data-driven approach to assess the interaction between both local and remote cortical areas during the use of an electrocorticographic BCI, a method which allows direct sampling of cortical surface potentials. Comparing the area controlling the BCI with remote areas, we evaluated relationships between the amplitude envelopes of band limited powers as well as non-linear phase-phase interactions. We found amplitude-amplitude interactions in the high gamma (HG, 70-150 Hz) range that were primarily located in the posterior portion of the frontal lobe, near the controlling site, and non-linear phase-phase interactions involving multiple frequencies (cross-frequency coupling between 8-11 Hz and 70-90 Hz) taking place over larger cortical distances. Further, strength of the amplitude-amplitude interactions decreased with time, whereas the phase-phase interactions did not. These findings suggest multiple modes of cortical communication taking place during BCI use that are specialized for function and depend on interaction distance.

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes.

Introduction: Continuous, minimally invasive monitoring of arterial health has significant potential for both improving our understanding of early progression of arteriosclerosis/atherosclerosis and enabling early treatment. Though devices exist that can report heart rate and heart rate variability in fully ambulatory settings, monitoring of pulse pressure wave morphology, including pulse wave velocity (PWV) and augmentation index (AIx) - two emerging indicators of arterial stiffness - is still currently limited to benchtop systems requiring an expert operator or cuff-based systems that are not well tolerated. Objective: To develop and validate a device capable of continuous monitoring of hemodynamic and electrophysiological signals, including computation of derived vascular stiffness indicators (peripheral PWV and AIx) comparable to current standards. Methods: We developed a wrist-worn device for continuous radial pulse wave capture and analysis (e.g., peripheral AIx computation) and pulse wave velocity measurement (measured from EKG landmarks to the radial pulse arrival time). The core sensors on the device are (1) a pressure sensor that rests over the radial artery, (2) a pair of dry EKG electrodes in lead-I configuration, and (3) a six-axis motion sensor. It includes a Bluetooth radio for data streaming, and sufficient on-board storage for 2 weeks of ambulatory studies. To validate the device against the current gold standard for radial tonometry, we recorded multiple 60-second sessions of tonometric data using both our device (2 sessions) and the standard Millar SPT-301 tonometer (3 sessions) in 28 subjects. We then compared AIx computed from these two devices. Results: Using our wrist-worn device we calculated AIx values that were highly consistent with those of the Millar tonometer (r = 0.927, mean difference ± SD of 0.026 ± 0.049). Conclusions: This platform enables continuous non-invasive hemodynamic monitoring. We intend to utilize it in future studies about how peripheral AIx and PWV behave over longer time periods, how they correlate to daily activities, including sleep, exercise, and food/medication intake, and - most importantly - whether they can serve as new, ambulatory indicators of cardiovascular risk.

Objective: Human voluntary movements are a final product of complex interactions between multiple sensory, cognitive and motor areas of central nervous system. The objective was to investigate temporal sequence of activation of premotor (PM), primary motor (M1) and somatosensory (S1) areas during cued finger movements.Methods: Electrocorticography (ECoG) was used to measure activation timing in human PM, S1, and M1 neurons in preparation for finger movements in 5 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150 Hz). The three cortical regions were mapped anatomically using a common brain atlas and confirmed independently with direct electrical cortical stimulation, somatosensory evoked potentials and detection of HG response to tactile stimulation. Subjects were given visual cues to flex each finger or pinch the thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between two electrodes and compute time lag. Statistical constraints were applied to the time estimates to combat the noise. Rank sum testing was used to verify the sequential activation of cortical regions across 5 subjects.Results: In all 5 subjects, HG activation in PM preceded S1 by an average of 53 +/- 13 ms (P = 0.03), PM preceded M1 by 180 +/- 40 ms (P = 0.001) and S1 activation preceded M1 by 136 +/- 40 ms (P = 0.04).Conclusions: Sequential HG activation of PM, S1 and M1 regions in preparation for movements is reported. Activity in S1 prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, i.e., an efference copy. Our analysis suggests that S1 modulation likely originates from PM.Significance: First electrophysiological evidence of efference copy in humans. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Continuous cardiac monitoring of healthy and unhealthy patients can help us understand the progression of heart disease and enable early treatment. Optical pulse sensing is an excellent candidate for continuous mobile monitoring of cardiovascular health indicators, but optical pulse signals are susceptible to corruption from a number of noise sources, including motion artifact. Therefore, before higher-level health indicators can be reliably computed, corrupted data must be separated from valid data. This is an especially difficult task in the presence of artifact caused by ambulation (e.g. walking or jogging), which shares significant spectral energy with the true pulsatile signal. In this manuscript, we present a machine-learning-based system for automated estimation of signal quality of optical pulse signals that performs well in the presence of periodic artifact. We hypothesized that signal processing methods that identified individual heart beats (segmenting approaches) would be more error-prone than methods that did not (nonsegmenting approaches) when applied to data contaminated by periodic artifact. We further hypothesized that a fusion of segmenting and nonsegmenting approaches would outperform either approach alone. Therefore, we developed a novel non-segmenting approach to signal quality estimation that we then utilized in combination with a traditional segmenting approach. Using this system we were able to robustly detect differences in signal quality as labeled by expert human raters (Pearson's r = 0.9263). We then validated our original hypotheses by demonstrating that our non-segmenting approach outperformed the segmenting approach in the presence of contaminated signal, and that the combined system outperformed either individually. Lastly, as an example, we demonstrated the utility of our signal quality estimation system in evaluating the trustworthiness of heart rate measurements derived from optical pulse signals.

Brain-computer interfaces (BCIs) are devices that record from the nervous system; provide input directly to the nervous system, or do both. Sensory BCIs such as cochlear implants have already had notable clinical success and motor BCIs have shown great promise for helping patients with severe motor deficits. Clinical and engineering outcomes aside, BCIs can also be tremendously powerful tools for scientific inquiry into the workings of the nervous system. They allow researchers to inject and record information at various stages of the system, permitting investigation of the brain in vivo and facilitating the reverse engineering of brain function. Most notably, BCIs are emerging as a novel experimental tool for investigating the tremendous adaptive capacity of the nervous system.

Motor-based brain-computer interface (BCI) use is a learned skill that has been shown to involve multiple cortical areas, though it only explicitly requires modulation of a small area of cortical tissue. The roles being played by these other areas have not yet been determined. In this study, using an electrocorticographic (ECoG) model, we apply a novel computational approach to quantifying the strength and nature of interactions between putative primary motor cortex and ventral premotor cortex (PMv) during BCI use and across BCI skill development. Our findings suggest multiple roles being carried out in PMv that change in strength and nature during skill acquisition.

Objective. Recently, electrocorticography-based brain-computer interfaces have been successfully used to translate cortical activity into control signals for external devices. However, the utility of such devices would be greatly enhanced by somatosensory feedback. Direct stimulation of somatosensory cortex evokes sensory perceptions, and is thus a promising option for closing the loop. Before this can be implemented in humans it is necessary to evaluate how changes in stimulus parameters are perceived and the extent to which they can be discriminated. Approach. Electrical stimulation was delivered to the somatosensory cortex of human subjects implanted with electrocorticography grids. Subjects were asked to discriminate between stimuli of different frequency and amplitude as well as to report the qualitative sensations elicited by the stimulation. Main results. In this study we show that in humans implanted with electrocorticography grids, variations in the amplitude or frequency of cortical electrical stimulation produce graded variations in percepts. Subjects were able to reliably distinguish between different stimuli. Significance. These results indicate that direct cortical stimulation is a feasible option for sensory feedback with brain-computer interface devices.

The majority of subjects who attempt to learn control of a brain–computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.

INTRODUCTION: Our inability to tickle ourselves has led to the hypothesis that, when an efferent signal is produced and sent to the motor system, a correlate, known as efference copy, is created to distinguish sensations produced by the subject's own actions from those generated by external stimuli. METHODS: To demonstrate the existence of such entity, we used electrocorticography (ECoG) to measure activation timing in human primary motor (M1), premotor (PM) and somatosensory (S1) neurons in preparation for finger movements in 4 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150 Hz). The 3 cortical regions were mapped anatomically using a common brain atlas and confirmed with sensory stimulation. Subjects were given visual cues to flex each finger or pinch thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between 2 electrodes and compute their time lag. To combat noise, we subjected both time estimates and electrode sorting to statistical constraints. We used rank sum testing to verify the sequential activation of these cortical regions across 4 subjects. RESULTS: In all 4 subjects, we found PM , S1 and M1 activated 385 (± 78), 312 (± 66), and 134 (± 34) msec ahead of finger movements. On average, PM activated approximately 251 msec ahead of M1 (P < .000), S1 activated approximately 178 msec ahead of M1 (P = .003) and PM lead S1 by approximately 73 msec (P = .11). CONCLUSION: Firing of S1 neurons prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, ie, an efferent copy. Our data suggests that this modulation of S1 is likely coming from PM.