Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer.

Pressure sensor-impregnated walkways transform a person's footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and without neuromotor pathology. Evidence shows that plantar pressure distributions are altered in aging and diabetic peripheral neuropathy, but less is known about pressure dynamics in chemotherapy-induced peripheral neuropathy (CIPN), a condition leading to falls in cancer survivors. Studying pressure dynamics longitudinally as people develop CIPN will require a composite model that can accurately characterize a survivor's gait consistencies before chemotherapy, even in the presence of normal step-to-step variation. In this paper, we present a state-of-the-art data-driven learning technique to identify consistencies in an individual's plantar pressure dynamics. We apply this technique to a database of steps taken by each of 16 women before they begin a new course of neurotoxic chemotherapy for breast or gynecologic cancer. After extracting gait features by decomposing spatiotemporal plantar pressure data into low-rank dynamic modes characterized by three features: frequency, a decay rate, and an initial condition, we employ a machine-learning model to identify consistencies in each survivor's walking pattern using the centroids for each feature. In this sample, our approach is at least 86% accurate for identifying the correct individual using their pressure dynamics, whether using the right or left foot, or data from trials walked at usual or fast speeds. In future work, we suggest that persistent deviation from a survivor's pre-chemotherapy step consistencies could be used to automate the identification of peripheral neuropathy and other chemotherapy side effects that impact mobility.

Exploring the acute cardiovascular effects of Floatation-REST.

The central nervous system (CNS) exerts a strong regulatory influence over the cardiovascular system in response to environmental demands. Floatation-REST (Reduced Environmental Stimulation Therapy) is an intervention that minimizes stimulation from the environment, yet little is known about the autonomic consequences of reducing external sensory input to the CNS. We recently found that Floatation-REST induces a strong anxiolytic effect in anxious patients while paradoxically enhancing their interoceptive awareness for cardiorespiratory sensations. To further investigate the physiologic nature of this anxiolytic effect, the present study measured acute cardiovascular changes during Floatation-REST using wireless and waterproof equipment that allowed for concurrent measurement of heart rate, heart rate variability (HRV), breathing rate, and blood pressure. Using a within-subjects crossover design, 37 clinically anxious participants with high levels of anxiety sensitivity and 20 non-anxious comparison participants were randomly assigned to undergo a 90-min session of either Floatation-REST or an exteroceptive comparison condition that entailed watching a relaxing nature film. Measures of state anxiety and serenity were collected before and after each session, while indices of autonomic activity were measured throughout each session. HRV was calculated using both time-series and frequency domain analyses. Linear mixed-effects modeling revealed a significant main effect of condition such that relative to the film condition, Floatation-REST elicited significant decreases (p < 0.001) in diastolic blood pressure, systolic blood pressure, breathing rate, and certain metrics of HRV including the standard deviation of the interbeat interval (SDNN), low-frequency HRV, and very low-frequency HRV. Heart rate showed a non-significant trend (p = 0.073) toward being lower in the float condition, especially toward the beginning of the session. The only metric that showed a significant increase during Floatation-REST was normalized high-frequency HRV (p < 0.001). The observed physiological changes were consistent across both anxious and non-anxious participants, and there were no significant group by condition interactions. Blood pressure was the only cardiac metric significantly associated with float-related reductions in state anxiety and increases in serenity. These findings suggest that Floatation-REST lowers sympathetic arousal and alters the balance of the autonomic nervous system toward a more parasympathetic state. Clinical trial registration: [https://clinicaltrials.gov/show/NCT03051074], identifier [NCT03051074].

Cortical Reorganization of Early Somatosensory Processing in Hemiparetic Stroke.

The cortical motor system can be reorganized following a stroke, with increased recruitment of the contralesional hemisphere. However, it is unknown whether a similar hemispheric shift occurs in the somatosensory system to adapt to this motor change, and whether this is related to movement impairments. This proof-of-concept study assessed somatosensory evoked potentials (SEPs), P50 and N100, in hemiparetic stroke participants and age-matched controls using high-density electroencephalograph (EEG) recordings during tactile finger stimulation. The laterality index was calculated to determine the hemispheric dominance of the SEP and re-confirmed with source localization. The study found that latencies of P50 and N100 were significantly delayed in stroke brains when stimulating the paretic hand. The amplitude of P50 in the contralateral (to stimulated hand) hemisphere was negatively correlated with the Fügl-Meyer upper extremity motor score in stroke. Bilateral cortical responses were detected in stroke, while only contralateral cortical responses were shown in controls, resulting in a significant difference in the laterality index. These results suggested that somatosensory reorganization after stroke involves increased recruitment of ipsilateral cortical regions, especially for the N100 SEP component. This reorganization delays the latency of somatosensory processing after a stroke. This research provided new insights related to the somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective.

Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-Frequency Phase Coupling.

BACKGROUND: Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as n : m phase locking value (where two coupling frequencies are linked as: mf 1 = nf 2) and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. f, 2f/3) and across three frequencies (e.g. f 1, f 2, f 1 + f 2), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. f 1, f 2, f 3, f 4, f 1 + f 2 + f 3 - f 4), or coupling that involves non-integer multiples of the frequencies (e.g. f 1, f 2, 2f 1/3 + f 2/3). NEW METHODS: To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators. RESULTS: The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled Rössler oscillators, as well as a human subject dataset. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems.

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal.

Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.

Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Taking the body off the mind: Decreased functional connectivity between somatomotor and default-mode networks following Floatation-REST.

Floatation-Reduced Environmental Stimulation Therapy (REST) is a procedure that reduces stimulation of the human nervous system by minimizing sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational, and proprioceptive channels, in addition to minimizing musculoskeletal movement and speech. Initial research has found that Floatation-REST can elicit short-term reductions in anxiety, depression, and pain, yet little is known about the brain networks impacted by the intervention. This study represents the first functional neuroimaging investigation of Floatation-REST, and we utilized a data-driven exploratory analysis to determine whether the intervention leads to altered patterns of resting-state functional connectivity (rsFC). Healthy participants underwent functional magnetic resonance imaging (fMRI) before and after 90 min of Floatation-REST or a control condition that entailed resting supine in a zero-gravity chair for an equivalent amount of time. Multivariate Distance Matrix Regression (MDMR), a statistically-stringent whole-brain searchlight approach, guided subsequent seed-based connectivity analyses of the resting-state fMRI data. MDMR identified peak clusters of rsFC change between the pre- and post-float fMRI, revealing significant decreases in rsFC both within and between posterior hubs of the default-mode network (DMN) and a large swath of cortical tissue encompassing the primary and secondary somatomotor cortices extending into the posterior insula. The control condition, an active form of REST, showed a similar pattern of reduced rsFC. Thus, reduced stimulation of the nervous system appears to be reflected by reduced rsFC within the brain networks most responsible for creating and mapping our sense of self.

Wireless Coexistence of Cellular LBT Systems and BLE 5.

The 2.4 GHz spectrum is home to several Radio Access Technologies (RATs), including ZigBee, Bluetooth Low Energy (BLE), and Wi-Fi. Accordingly, the technologies' spectrum-sharing qualities have been extensively studied in literature. License-Assisted Access (LAA) Listen-Before-Talk (LBT) has been identified in technical reports as the foundation for the channel access mechanism for 5G New Radio-Unlicensed (NR-U) operating in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band. The introduction of NR-U into this band raises new concerns regarding coexistence of the newcomer with traditional incumbents. This article reports an investigation of BLE 5 and cellular LBT coexisting systems by means of empirical evaluation. The importance of this study stems from that the studied LBT mechanism is indicative of how 5G NR-U would perform in the 2.4 GHz band. Tests were performed in conformity with the American National Standards Institute (ANSI) C63.27 standard for evaluation of wireless coexistence, and results were reported in terms of throughput and interframe delays. In accordance with the standard and under different BLE physical layers (PHYs) and LBT priority classes, three setups were investigated. These pertain to the three tiers of evaluation, which correspond to the criticality of the device under test. Results demonstrated how BLE throughput dropped as the intended-to-unintended signal ratio decreased, and LBT classes exhibited a diminishing effect as the class priority descended. Long Range BLE PHY was found to sustain longer gap times (i.e., delay) than the other two PHYs; however, it showed less susceptibility to interference. Results also demonstrated that low data rate BLE PHYs hindered the LBT throughput performance since they correspond to longer airtime durations.

Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure.

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

5G NR-U: Homogeneous Coexistence Analysis.

Current technical reports indicate License-Assisted Access (LAA) Listen-Before-Talk (LBT) as the preferred channel access scheme for the upcoming 5G New Radio-Unlicensed. Various studies have examined heterogeneous coexistence of WiFi/LTE-LAA systems. This paper investigates the homogeneous coexistence of intra-network LAA-LBT devices operating in dense deployment scenarios. Results relevant to ETSI-specified priority classes are reported in terms of channel utilization, collision probability, and channel access delay. The framework presented in this paper is then employed to investigate wireless coexistence in a 5G-enabled intensive care unit employing remote patient monitoring over 5G NR-U.

Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network.

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision-making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self-regulate the vmPFC activity using a real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI-nf signal represented as variable-height bar). Individuals were instructed to raise the bar by self-relevant value-based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer-generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI-nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task-positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self-regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.

EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects.

Electroencephalography (EEG) measures the brain's electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts' brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

PESA: Probabilistic Efficient Storage Algorithm for Time-Domain Spectrum Measurements.

Wireless communication is an essential part of daily life for users globally with applications in medical devices, cellular phones, Internet of Things nodes, and others. Accordingly, there is a need to understand the patterns and properties of radio frequency spectrum use by acquiring accurate spectrum utilization measurements. However, the massive storage volume needed to execute spectrum surveys-especially when a fast sampling rate is used-is an impeding factor in terms of cost and ease-of-access. In this article, a probabilistic efficient storage algorithm (PESA) is proposed to facilitate high-accuracy, time-domain spectrum surveys conducted at a fast sample acquisition rate to detect sporadic spectrum occupancy patterns that could be on the order of microseconds. PESA divides the dynamic range of a monitoring equipment into bins-each represented by one component of a Gaussian mixture model (GMM). Windows of activity and inactivity in the measurements are established by comparing with a threshold and then indicators to the GMM component that best describes a window are recorded. Hence, reducing required storage volume. Results demonstrate that ≈ 99% reduction in storage volume is achievable while maintaining an accurate estimation of channel utilization and activity/inactivity periods. Furthermore, a Lab-VIEW implementation of PESA on a hardware platform was executed and used to survey Wi-Fi channel 1 in a healthcare environment for seven consecutive hours. Although more than 25 billion samples were observed, resulting data only occupied 96.28 megabytes.

Predicting Age From Brain EEG Signals-A Machine Learning Approach.

Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction. Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced. Results: The stack-ensemble age prediction model achieved R2 = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds. Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.

On the Coexistence of LTE-LAA in the Unlicensed Band: Modeling and Performance Analysis.

Long term evolution (LTE) technology leveraging the unlicensed band is anticipated to provide a solution for the challenges stemming from the rapid growth of mobile wireless services, the scarcity of available licensed spectrum, and the expected significant increase in mobile data traffic. Ensuring fair operation in terms of spectrum sharing with current unlicensed spectrum incumbents is a key concern relative to the success and viability of Unlicensed LTE (U-LTE). This paper addresses the problem of modeling and evaluating the coexistence of LTE license-assisted-access in the unlicensed band. The paper presents a novel analytical model using Markov chain to accurately model the LAA listen-before-talk scheme, as specified in the final technical specification 36.213 of 3GPP release 13 and 14. Furthermore, model validation is demonstrated through numerical and simulation results comparison. Model performance evaluation is examined and contrasted with IEEE 802.11 distributed coordination function. Finally, a comprehensive coexistence performance analysis is conducted for both homogeneous and heterogeneous network scenarios and coexistence results are presented and discussed herein.

Estimating the Likelihood of Wireless Coexistence Using Logistic Regression: Emphasis on Medical Devices.

Medical device manufacturers incorporate wireless technology in their designs to offer convenience and agility to both patients and caregivers. However, the use of unlicensed radio spectrum bands by both medical devices and other equipment raises concerns about wireless coexistence. Work by the accredited standards committee C63 of the American National Standards Institute (ANSI) to provide the community with a consensus standard for coexistence evaluation resulted in the publication of the ANSI C63.27 standard, which was later recognized by the U.S. Food and Drug Administration. Estimating the likelihood of wireless coexistence of a system under test (SUT) in a given environment is central to the evaluation and reporting of wireless coexistence, as made clear in the C63.27 standard. However, no method to perform this estimation is provided. In this paper, we propose the use of logistic regression (LR) to estimate the likelihood of wireless coexistence of a medical device in its intended environment. Radiated open environment coexistence testing was used to realize a test scenario in which the interfering network was IEEE 802.11n Wi-Fi and the SUT was ZigBee; exemplary wireless technologies for interfering network and medical device, respectively. LR model fitting was then performed to derive a model that describes the performance of SUT under a range of wireless coexistence phenomena. Finally, results were incorporated with the outcome of a spectrum survey using Monte Carlo simulation to estimate the SUT likelihood of wireless coexistence in a hospital environment.

Practical aspects of wireless medical device coexistence testing.

Integrating wireless technology in medical devices has proved beneficial for both patients and caregivers. However, the use of shared, unlicensed spectrum bands by both medical and non-medical wireless devices has raised concerns about wireless coexistence. The challenge of incorporating wireless communication into a medical device is to ensure reasonable medical device effectiveness and patient safety. Consequently, work to develop a standardized process to assess wireless coexistence, primarily for wireless medical devices, was carried by Subcommittee 7 of American National Standards Institute (ANSI)-accredited standards committee (ASC) C63 and the Wireless Working Group (SM-WG06) of the Association for the Advancement of Medical Instrumentation (AAMI). Both groups have recently released their respective documents. In this article, we discuss practical aspects of wireless coexistence testing-in the realm of ANSI C63.27 and AAMI TIR69-to help answer basic, yet important, questions such as what to test, how to test, and how to present results.

Characterizing the 2.4 GHz Spectrum in a Hospital Environment: Modeling and Applicability to Coexistence Testing of Medical Devices.

The increasing use of shared, unlicensed spectrum bands by medical devices and nonmedical products highlights the need to address wireless coexistence to ensure medical device safety and effectiveness. This paper provides the first step to approximate the probability of a device coexisting in its intended environment by providing a generalized framework for modeling the environment. The application of this framework is shown through an 84-day spectrum survey of the 2.4-2.48 GHz industrial, scientific, and medical band in a hospital environment in the United States. A custom platform was used to monitor power flux spectral density and record received power. Channel utilization of three nonoverlapping channels of 20 MHz bandwidth-relative to IEEE 802.11 channels 1, 6, and 11-were calculated and fitted to a generalized extreme value distribution. Low channel utilization was observed (<10%) in the surveyed environment with sporadic occurrences of higher channel utilization (>50%). Reported findings can be complementary to wireless coexistence testing. This paper can provide input to the development of a consensus standard for wireless device coexistence test methods and a consensus document focused on wireless medical device coexistence risk management.

Real-time EEG artifact correction during fMRI using ICA.

BACKGROUND: Simultaneous acquisition of EEG and fMRI data results in EEG signal contamination by imaging (MR) and ballistocardiogram (BCG) artifacts. Artifact correction of EEG data for real-time applications, such as neurofeedback studies, is the subject of ongoing research. To date, average artifact subtraction (AAS) is the most widespread real-time method used to partially remove BCG and imaging artifacts without requiring extra hardware equipment; no alternative software-only real time methods for removing EEG artifacts are available. NEW METHODS: We introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA). The rtICA is based on real time independent component analysis (ICA) and it is employed following the AAS method. The rtICA was implemented and validated during EEG and fMRI experiments on healthy subjects. RESULTS: Our results demonstrate that the rtICA employed after the rtAAS can obtain 98.4% success in detection of eye blinks, 4.4 times larger INPS reductions compared to RecView-corrected data, and effectively reduce motion artifacts, as well as imaging and muscle artifacts, in real time on six healthy subjects. COMPARISON WITH EXISTING METHODS: We compared our real-time artifact reduction results with the rtAAS and various offline methods using multiple evaluation metrics, including power analysis. Importantly, the rtICA does not affect brain neuronal signals as reflected in EEG bands of interest, including the alpha band. CONCLUSIONS: A novel real-time ICA method was proposed for improving the EEG quality signal recorded during fMRI acquisition. The results show substantial reduction of different types of artifacts using real-time ICA method.

Wide-area and omnidirectional optical detector arrays using modular optical elements.

This paper presents novel, modular optical detector arrays of various shapes and configurations. Recently developed Modular Optical Wireless Elements (MOWE) architecture serves as the basis for large and complex optical detector arrays that can be constructed as geometric shells and provide wide-area even omnidirectional field-of-view (FoV). Programmable optical modules synchronously sample the environment, and then route measurements to the user through a dedicated electrical backbone. The arrays are inexpensive, easy to construct, and can be made with homogeneous/inhomogeneous optical properties. Applications include remote sensing, motion detection, optical navigation, and medical imaging, among others. We present the MOWE detector array concept with a detailed optical analysis and a suggested design methodology, as well as a number of various demonstrations. We also utilize wavelength-diversity of MOWE arrays to demodulate two overlapping signals, showing that many diversity-based algorithms can be conveniently prototyped and implemented.