Short Data Segments Research Articles

Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under non-stationary head conditions

Objective:This work addresses an impractical and unnatural constraint that has been generally enforced in state-of-the-art electrooculography (EOG)-based gaze estimation methods, that of maintaining a stationary head pose and position. Specifically, this work proposes an EOG-based gaze angle (GA) estimation method that accommodates natural variations in the user’s head pose and position. The EOG data collected under non-stationary head conditions, which was used in this work to validate the proposed method, is also being made publicly available. Methods:This work generalises a two-eye verging gaze geometrical model to cater for arbitrary head poses and positions, and also models the dynamics of the vestibulo-ocular reflex (VOR), which refers to the eye-head coordination that normally takes place during gaze shifts under unrestrained head conditions. These methods are validated by incorporating them within a published multiple-model framework for GA estimation. Results:When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.85 ± 0.51° and 2.19 ± 0.62°, respectively, and an eye movement detection and labelling F-score close to 90% were obtained. These results are comparable to those reported previously under stationary head conditions. Conclusion:This work demonstrates that accurate GA estimation and eye movement detection and labelling can be achieved using EOG signals, even when the user’s head is not stationary. Significance:This work eliminates the need for users to maintain a stationary head pose and position, a common constraint in the field, thus introducing an EOG-based GA estimation method that allows users to move their heads naturally.

Online State of Health Estimation with Deep Learning Frameworks Based on Short and Random Battery Charging Data Segments

Lithium-ion (Li-ion) batteries find wide application across various domains, ranging from portable electronics to electric vehicles (EVs). Reliable online estimation of the battery’s state of health (SOH) is crucial to ensure safe and economical operation of battery-powered devices. Here, we developed three deep learning models to investigate their potential for online SOH estimation using partial and random charging data segments (voltage and charging capacity). The models employed were developed from the feed-forward neural network (FNN), the convolutional neural network (CNN) and the long short-term memory (LSTM) neural network, respectively. We show that the proposed deep learning frameworks can provide flexible and reliable online SOH estimation. Particularly, the LSTM-based estimation model exhibits superior performance across the test set in both direct learning and transfer learning scenarios, while the CNN and FNN-based models show slightly diminished performance, especially in the complex transfer learning scenario. The LSTM-based model achieves a maximum estimation error of 1.53% and 2.19% in the direct learning and transfer learning scenarios, respectively, with an average error as low as 0.28% and 0.30%. Our work highlights the potential for conducting online SOH estimation throughout the entire life cycle of Li-ion batteries based on partial and random charging data segments.

Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under stationary head conditions

Objective:This work aims to propose a novel method to estimate the gaze from electrooculography (EOG) signals while compensating for the baseline drift, and which intrinsically detects fixations, saccades and blinks. In contrast to existing baseline drift mitigation techniques, the proposed framework is real-time-implementable, and does not require the average point of gaze to lie at the primary gaze position, nor does it disrupt the overall ocular pose-induced EOG signal DC characteristics. The EOG data used to validate the proposed method is also being made publicly available. Methods:The proposed method is based on the dual Kalman filter, which estimates the gaze angles (GAs) and the baseline concurrently, taking into consideration the EOG signal’s non-stationary and temporally-multimodal characteristics. In fact, it is a multiple-model technique based on a battery model of the eye wherein fixations, saccades and blinks are modelled separately. Results:When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.64 ± 0.82°and 1.97 ± 0.34°, respectively, was obtained, which compared well with the corresponding results obtained using the state-of-the-art linear regression models. Conversely, for longer data segments, the proposed framework yielded superior GA estimation performance when compared to the state-of-the-art techniques. Eye movement detection and labelling F-scores exceeding 90% were achieved. Conclusion:The proposed method yields reliable gaze estimation performance, and accurately detects fixations, saccades and blinks. Significance:This work proposes an integrated method to simultaneously estimate the GAs and address the baseline drift issue without the limitations of existing baseline drift mitigation techniques.

Exhaust-gas temperature model and prognostic feature for diesel engines

Prognostics is an enabler of predictive maintenance, which can improve reliability, availability, and reduce the maintenance cost of high value assets. A novel prognostic feature was developed that tracks diesel fuel injector damage based upon measured exhaust gas temperature for each cylinder, where prior work was limited to fault detection. The feature value is computed using a simple and accurate two-stage hybrid linear regression model for exhaust gas temperature that is based on engine thermodynamics and data from the engine controller. The model is shown to be accurate to within 10 percent at low exhaust temperature and within 5 percent at high exhaust temperature. The study showed that the feature has a strong correlation to injector leakage over the leakage range of 0–100 mm3 per cylinder stroke. The feature was developed from steady-state operating conditions and is shown to generalize to operating conditions outside of the development set. It was also shown to work on short stable data segments (3 s and longer) extracted from transient operating conditions.

Heart Rate Variability by Dynamical Patterns in Windows of Holter Electrocardiograms: A Method to Discern Left Ventricular Hypertrophy in Heart Transplant Patients Shortly after the Transplant

Background: The Holter electrocardiogram (ECG) provides a long signal that represents the heart’s responses to both autonomic regulation and various phenomena, including heart tissue remodeling. Loss of information is a common result when using global statistical metrics. Method: Breaking the signal into short data segments (e.g., windows) provides access to transient heart rate characteristics. Symbolization of the ECG by patterns of accelerations and/or decelerations allows using entropic metrics in the assessment of heart rate complexity. Two types of analysis are proposed: (i) visualization of the pattern dynamics of the whole signal, and (ii) scanning the signal for pattern dynamics in a sliding window. The method was applied to a cohort of 42 heart transplant (HTX) recipients divided into the following groups: a left ventricle of normal geometry (NG), concentrically remodeled (CR), hypertrophic remodeled (H), and to the control group (CG) consisting of signals of 41 healthy coevals. The Kruskal–Wallis test was used to assess group differences. Statistical conclusions were verified via bootstrap methods. Results: The visualization of the group pattern dynamics showed severely limited autonomic regulations in HTX patients when compared to CG. The analysis (in segments) prove that the pattern dynamics of the NG group are different from the pattern dynamics observed in the CR and H groups. Conclusion: Dynamic pattern entropy estimators tested in moving windows recognized left ventricular remodeling in stable HTX patients.

On the potential of offshore sensors and array processing for improving seismic event detection and locations in the North Sea

SUMMARY The capability to detect and locate seismicity in the North Sea is necessary to ensure the safety of future CO2 storage operations. Currently, the event location capabilities are hampered by a one-sided network configuration, as only sensors deployed on the Norwegian mainland are providing good signal-to-noise ratios. However, applying array processing techniques to groups of offshore sensors deployed at oil and gas reservoirs can improve the signal-to-noise ratio at such sites, which generally suffer from high noise contamination originating from platforms or seismic shooting. In this study we test the feasibility of array processing to sensors deployed at the Grane oil field to improve earthquake event locations. Out of the 3400 nodes installed, we have access to two data subsets: (i) continuous data from 10 sensors spread out over the whole field and (ii) short segments of data from 30 sensors selected with the purpose to apply array methods. As the average interstation distance between the 10 sensors is 6 km, the recorded wavefield is not coherent and traditional array processing methods are not applicable. To optimize the usage of the 10-sensor offshore data for event location purposes, we develop a new method for incoherent array processing. We compute a characteristic function based on the kurtosis of the seismic traces prior to a frequency–wavenumber analysis. This method successfully determines slowness and backazimuth for almost all the evaluated earthquakes in this study. Using common coherent array processing, the 30 sensors show superior detection capabilities over the 10 sensors and will therefore decrease the detection threshold for seismic events in the region significantly if continuous data are available. We conclude that offshore sensors at Grane can be included as a part of a system for passive seismic monitoring in the North Sea. We recommend using the 30-sensors data for this purpose. However, as we only have access to continuous data from the 10 sensors in this study, we found that they are a good substitute when the 30-sensor data are not available.

An Improved Altimeter in-Orbit Range Noise-Level Estimation Approach Based on Along-Track Differential Method

Satellite radar altimeters are advanced remote sensing devices that play an important role in observing the global marine environment. Accurately estimating the noise level of altimeter in-orbit ranging data is crucial for evaluating the payload performance, analyzing sea conditions, and monitoring data quality. In this study, we propose an approach based on the differential processing of along-track odd–even data sequences for altimeter in-orbit range noise-level estimation. Using the long-term along-track data sequence can notably improve the issue in the existing method in that the noise level is underestimated owing to the utilization of a relatively short data segment. On the basis of an analysis of the influence of low-frequency components on noise-level estimation, the mathematical formulas of the above differential method were deduced, and the efficacy of the approach in assessing the noise level of altimeter in-orbit data was demonstrated by simulation experiments. This method was used to estimate the noise levels of the 20 Hz datasets of Jason-3 and Sentinel-6, and the idea of the time-domain difference was extended to the frequency domain. The statistical results showed that the 20 Hz noise levels at the significant wave height (SWH) = 2 m were 7.41 cm (Jason-3 low-resolution (LR) mode), 6.66 cm (Sentinel-6 LR mode), and 3.13 cm (Sentinel-6 high-resolution (HR) mode). The power spectrum density analysis further verified its accuracy. By reprocessing the 20 Hz data of Sentinel-6 into 10, 5, and 1 Hz, the effectiveness of the along-track odd–even differential method to directly evaluate the noise level of 1 Hz data was explored, and the impact of ocean signals such as swells on noise-level estimation in synthetic aperture mode was discussed.

Functional relationship of arterial blood pressure, central venous pressure and intracranial pressure in the early phase after subarachnoid hemorrhage.

Intracranial pressure (ICP) and arterial blood pressure (ABP) are related to each other through cerebral autoregulation. Central venous pressure (CVP) is often measured to estimate cardiac filling pressures as an approximate measure for the volume status of a patient. Prior modelling efforts have formalized the functional relationship between CVP, ICP and ABP. However, these models were used to explain short segments of data during controlled experiments and have not yet been used to explain the slowly evolving ICP increase that occurs typically in patients after aneurysmal subarachnoid hemorrhage (SAH). To analyze the functional relationship between ICP, ABP and CVP recorded from SAH patients in the first five days after aneurysm. Two methods were used to elucidate this relationship on the running average of the signals: First, using Spearman correlation coefficients calculated over 30min segments Second, for each patient, linear state space models of ICP as the output and ABP and CVP as inputs were estimated. The mean and variance of the data and the correlation coefficients between ICP-ABP and ICP-CVP vary over time as the patient progresses through their stay in the ICU. On average, after an SAH event, the models show that a) ABP is the bigger driver of changes in ICP than CVP and that increasing ABP leads to reduction in ICP and (b) increasing CVP leads to an increase in ICP. Finding a) agrees with the hypothesis that patients with subarachnoid hemorrhage have defective autoregulation, and b) agrees with the positive correlation observed between central venous pressure and intracranial pressure in the literature.

First Sagittarius A* Event Horizon Telescope Results. IV. Variability, Morphology, and Black Hole Mass

In this paper we quantify the temporal variability and image morphology of the horizon-scale emission from Sgr A*, as observed by the EHT in 2017 April at a wavelength of 1.3 mm. We find that the Sgr A* data exhibit variability that exceeds what can be explained by the uncertainties in the data or by the effects of interstellar scattering. The magnitude of this variability can be a substantial fraction of the correlated flux density, reaching ∼100% on some baselines. Through an exploration of simple geometric source models, we demonstrate that ring-like morphologies provide better fits to the Sgr A* data than do other morphologies with comparable complexity. We develop two strategies for fitting static geometric ring models to the time-variable Sgr A* data; one strategy fits models to short segments of data over which the source is static and averages these independent fits, while the other fits models to the full data set using a parametric model for the structural variability power spectrum around the average source structure. Both geometric modeling and image-domain feature extraction techniques determine the ring diameter to be 51.8 ± 2.3 μas (68% credible intervals), with the ring thickness constrained to have an FWHM between ∼30% and 50% of the ring diameter. To bring the diameter measurements to a common physical scale, we calibrate them using synthetic data generated from GRMHD simulations. This calibration constrains the angular size of the gravitational radius to be μas, which we combine with an independent distance measurement from maser parallaxes to determine the mass of Sgr A* to be M ⊙.

Swarm Langmuir probes' data quality validation and future improvements

Abstract. Swarm is the European Space Agency (ESA)'s first Earth observation constellation mission, which was launched in 2013 to study the geomagnetic field and its temporal evolution. Two Langmuir probes aboard each of the three Swarm satellites provide in situ measurements of plasma parameters, which contribute to the study of the ionospheric plasma dynamics. To maintain a high data quality for scientific and technical applications, the Swarm products are continuously monitored and validated via science-oriented diagnostics. This paper presents an overview of the data quality of the Swarm Langmuir probes' measurements. The data quality is assessed by analysing short and long data segments, where the latter are selected to be sufficiently long enough to consider the impact of the solar activity. Langmuir probe data have been validated through comparison with numerical models, other satellite missions, and ground observations. Based on the outcomes from quality control and validation activities conducted by ESA, as well as scientific analysis and feedback provided by the user community, the Swarm products are regularly upgraded. In this paper, we discuss the data quality improvements introduced with the latest baseline, and how the data quality is influenced by the solar cycle. In particular, plasma measurements are more accurate in day-side regions during high solar activity, while electron temperature measurements are more reliable during night side at middle and low latitudes during low solar activity. The main anomalies affecting the Langmuir probe measurements are described, as well as possible improvements in the derived plasma parameters to be implemented in future baselines.

A Data-Driven Approach to Transfer Function Analysis for Superior Discriminative Power: Optimized Assessment of Dynamic Cerebral Autoregulation.

Transfer function analysis (TFA) is extensively used to assess human physiological functions. However, extracting parameters from TFA is not usually optimized for detecting impaired function. In this study, we propose to use data-driven approaches to improve the performance of TFA in assessing blood flow control in the brain (dynamic cerebral autoregulation, dCA). Data were collected from two distinct groups of subjects deemed to have normal and impaired dCA. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) were simultaneously recorded for approximately 10 mins in 82 subjects (including 41 healthy controls) to give 328 labeled samples of the TFA variables. The recordings were further divided into 4,294 short data segments to generate 17,176 unlabeled samples of the TFA variables. We optimized TFA post-processing with a generic semi-supervised learning strategy and a novel semi-supervised stacked ensemble learning (SSEL) strategy for classification into normal and impaired dCA. The generic strategy led to a performance with no significant difference to that of the conventional dCA analysis methods, whereas the proposed new strategy boosted the performance of TFA to an accuracy of 93.3%. To our knowledge, this is the best dCA discrimination performance obtained to date and the first attempt at optimizing TFA through machine learning techniques. Equivalent methods can potentially also be applied to assessing a wide spectrum of other human physiological functions.

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

Objective. Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques. Approach. We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80–250 Hz) were detected in all EEG data using an automated algorithm. Main results. HFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5 min−1 and 2.9 min−1, respectively; p = 0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98 min−1 and 0.82 min−1, respectively). Spatially, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs. Significance. Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.

Quantitative comparison of time-varying system identification methods to describe human joint impedance

Accurate and swift tuning of joint impedance is crucial to perform movement and interaction with our environment. Time-varying system identification enables quantification of joint impedance during movement. Many methods have been developed over the years, each with their own mathematical approach and underlying assumptions. Yet, for the identification of joint impedance, a systematic comparison revealing each method's unique strengths and weaknesses, is lacking. Here, we propose a quantitative framework to compare these methods.The framework is used to review five time-varying system identification methods using both simulated data and experimental data. These methods included three time-domain methods: ensemble, short data segment, and basis impulse response function; and two frequency-domain methods: ensemble spectral, and kernel-based regression. In the simulation study, joint stiffness – the static component of impedance – was simulated as a square wave to mimic the most extreme case of time-varying behavior. The identification results were compared based on the (1) variance accounted for (VAF), (2) bias, (3) random, and (4) total estimation error with respect to the simulated joint stiffness; and (5) rise time between two stiffness levels. In the experimental study, human ankle joint impedance was identified. Identification performance was compared using the variability in estimating joint stiffness – representative of the random error – and VAF.The performance metrics revealed distinct identification properties for each method. Therefore, researchers must make a well-justified decision which method is most appropriate for their application. The combination of simulation and experimental work with extensive performance quantification creates a framework for quantitative assessment of newly developed time-varying system identification methods.

Fast EEG-Based Decoding Of The Directional Focus Of Auditory Attention Using Common Spatial Patterns.

Noise reduction algorithms in current hearing devices lack informationabout the sound source a user attends to when multiple sources are present. To resolve this issue, they can be complemented with auditory attention decoding (AAD) algorithms, which decode the attention using electroencephalography (EEG) sensors. State-of-the-art AAD algorithms employ a stimulus reconstruction approach, in which the envelope of the attended source is reconstructed from the EEG and correlated with the envelopes of the individual sources. This approach, however, performs poorly on short signal segments, whilelonger segments yield impractically long detection delays when the user switches attention. We propose decoding the directional focus of attention using filterbank common spatial pattern filters (FB-CSP) as an alternative AAD paradigm, whichdoes not require access to the clean source envelopes. The proposed FB-CSP approach outperforms both the stimulus reconstruction approach on short signal segments, as well as a convolutional neural network approach on the same task. We achieve a high accuracy (80% for [Formula: see text] windows and 70% for quasi-instantaneous decisions), which is sufficient to reach minimal expected switch durations below [Formula: see text]. We also demonstrate that the decoder can adapt to unlabeled data from anunseen subject and works with only a subset of EEG channels located around the ear to emulate a wearable EEG setup. The proposed FB-CSP method provides fast and accurate decoding of the directional focus of auditory attention. The high accuracy on very short data segments is a major step forward towards practical neuro-steered hearing devices.

Determining the number of segments for piece-wise linear representation of discrete-time signals

Piece-wise linear representation (PLR) separates a discrete-time continuous-valued signal into a few short data segments, each of which is represented by a straight line. One prerequisite parameter in the PLR is the number of data segments. This paper proposes a new method to determine the number of data segments. The method is based on a key observation on two balancing effects of the number of data segments on the confidence intervals of line approximations. The confidence intervals define parallelogram-shaped spaces, and an index is formulated as the weighted percentage of these spaces occupied by data points in data segments. The number of data segments is determined as the one achieving the maximum value of the formulated index. Numerical and industrial examples are provided to illustrate the effectiveness of the proposed method.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.

An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis.

Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool.Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS.Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations.Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

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OBJECTIVES/SPECIFIC AIMS: To investigate the performance of a metric for passive localization of central sulcus. METHODS/STUDY POPULATION: We studied 7 patients with intractable epilepsy undergoing intra-cranial EEG (icEEG) monitoring at Yale, in whom central sulcus (CS) localization was obtained by standard methods. Our method takes advantage of inherent properties of the primary motor cortex (MC), which exhibits enhanced icEEG band-power and coherence across the CS. For each contact x we calculated the z-score of a composite power and synchrony value log10(px)x;cx, where px is sum of the root mean square of the icEEG in the high gamma band (80–115 Hz) for contact x over the 6–10 minutes of NREM sleep studied, and cx is the mean magnitude squared coherence in the same band using a 500-ms Hamming window between contact x and all other contacts. z-score values lower than threshold (th) were set to 0. Finally, we calculated a metric m=z/d, where d is the mean Euclidian distance of each contact from contacts with z scores greater than 0. The last step was implemented to emphasize local network activity. RESULTS/ANTICIPATED RESULTS: We report the results of a pilot study to test the performance of a new operator independent method for passive identification of CS with intractable epilepsy undergoing icEEG monitoring at Yale, in whom CS localization was obtained by standard methods. The sensori-motor (SM) cortex exhibited higher EEG-gamma power compared with non-SM cortex (p<0.0002). There was no significant difference between the motor/premotor and sensory cortex (p<0.47). CS was successfully localized in all patients with thresholds between 0.4 and 0.6. In 2 patients, knowledge of anatomy was needed to distinguish the MC from adjacent epileptic foci. The primary hand and leg motor areas exhibited the highest metric values consistently followed by the tongue motor area. Higher threshold values were very specific (94%) for the anterior bank of the CS but not sensitive. Intermediate threshold values achieved a reasonable trade-off (0.4: 89% specific and 70% sensitive). DISCUSSION/SIGNIFICANCE OF IMPACT: We present and successfully implement a rapid procedure for task-free and stimulation free localization of the central sulcus during sleep based on intrinsic electrophysiological properties of the primary motor strip which exhibits increased power and enhanced local connectivity. We successfully localized the central sulcus in all patients. When implementing appropriate thresholds, our proposed metric M is very specific for the anterior lip of the central sulcus which may make it ideal to identify this important anatomical landmark. Our method is sensitive for epileptogenic regions as well, therefore basic knowledge about central sulcus anatomy may be needed in cases where there is an epileptogenic lesion in the vicinity of the central suclus. Our method makes a few a priori assumptions: The regions around the central sulcus are adequately sampled and the occipital or parieto-occipital regions are not included in the analysis. In order for the method to function properly, nonsensori-MC should be sampled adequately as well. In the future, normative data could be generated for the composite product of connectivity×power which may replace within-patient z-scoring. Our method is rapid and can be implemented on short segments of ECoG data. The proposed method may be potentially used for identification of seeds in the motor cortex for subsequent network analysis and further studies may delineate its potential use in the operating room.

Reliability of EEG Measures of Interaction: A Paradigm Shift Is Needed to Fight the Reproducibility Crisis

Measures of interaction (connectivity) of the EEG are at the forefront of current neuroscientific research. Unfortunately, test-retest reliability can be very low, depending on the measure and its estimation, the EEG-frequency of interest, the length of the signal, and the population under investigation. In addition, artifacts can hamper the continuity of the EEG signal, and in some clinical situations it is impractical to exclude artifacts. We aimed to examine factors that moderate test-retest reliability of measures of interaction. The study involved 40 patients with a range of neurological diseases and memory impairments (age median: 60; range 21–76; 40% female; 22 mild cognitive impairment, 5 subjective cognitive complaints, 13 temporal lobe epilepsy), and 20 healthy controls (age median: 61.5; range 23–74; 70% female). We calculated 14 measures of interaction based on the multivariate autoregressive model from two EEG-recordings separated by 2 weeks. We characterized test-retest reliability by correlating the measures between the two EEG-recordings for variations of data length, data discontinuity, artifact exclusion, model order, and frequency over all combinations of channels and all frequencies, individually for each subject, yielding a correlation coefficient for each participant. Excluding artifacts had strong effects on reliability of some measures, such as classical, real valued coherence (~0.1 before, ~0.9 after artifact exclusion). Full frequency directed transfer function was highly reliable and robust against artifacts. Variation of data length decreased reliability in relation to poor adjustment of model order and signal length. Variation of discontinuity had no effect, but reliabilities were different between model orders, frequency ranges, and patient groups depending on the measure. Pathology did not interact with variation of signal length or discontinuity. Our results emphasize the importance of documenting reliability, which may vary considerably between measures of interaction. We recommend careful selection of measures of interaction in accordance with the properties of the data. When only short data segments are available and when the signal length varies strongly across subjects after exclusion of artifacts, reliability becomes an issue. Finally, measures which show high reliability irrespective of the presence of artifacts could be extremely useful in clinical situations when exclusion of artifacts is impractical.

Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus

ObjectiveTo investigate the possibility of tremor detection based on deep brain activity. MethodsWe re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features. ResultsApplying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature. ConclusionsLFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used. SignificanceLFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation.