An information-theoretic framework for conditional causality analysis of brain networks.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

This paper introduces a theoretical framework for causal inference in brain networks using time series measurements based on the principle of minimum-entropy regression. Three types of conditional causality measures are derived based on varying formulations of minimum-entropy regressions. The standard time-domain conditional Granger causality measure is formulated as a special case but with a different expression of the frequency-domain measure. The methods were evaluated using simulations and real resting-state functional MRI data of human brains and compared with standard Granger causality measures and directed transfer functions. Two new formulations of minimum-entropy-based causality measures showed better performance than other methods. The algorithms developed from this work may provide new insights to understand information flow in brain networks.

State Space Representation of Jiles-Atherton Hysteresis Model and Application for Closed-Loop Control.

Hysteresis is a fundamental characteristic of magnetic materials. The Jiles-Atherton (J-A) hysteresis model, which is known for its few parameters and clear physical interpretations, has been widely employed in simulating hysteresis characteristics. To better analyze and compute hysteresis behavior, this study established a state space representation based on the primitive J-A model. First, based on the five fundamental equations of the J-A model, a state space representation was established through variable substitution and simplification. Furthermore, to address the singularity problem at zero crossings, local linearization was obtained through an approximation method based on the actual physical properties. Based on these, the state space model was implemented using the S-function. To validate the effectiveness of the state space model, the hysteresis loops were obtained through COMSOL finite element software and tested on a permalloy toroidal sample. The particle swarm optimization (PSO) method was used for parameter identification of the state space model, and the identification results show excellent agreement with the simulation and test results. Finally, a closed-loop control system was constructed based on the state space model, and trajectory tracking experiments were conducted. The results verify the feasibility of the state space representation of the J-A model, which holds significant practical implications in the development of magnetically shielded rooms, the suppression of magnetic interference in cold atom clocks, and various other applications.

Perfect control for nonminimum-phase unstable systems defined in the multivariable continuous-time state-space framework - An inverse-based approach.

In this paper, a new perfect control law dedicated to nonminimum-phase unstable LTI MIMO systems governed in the continuous-time state-space domain is proposed. Two algorithms are investigated, one of which has turned out to be definitely accurate. Henceforth, the inverse model control-based formula can be applied to any right-invertible plants having more input than output variables. Last, not least, through the application of some generalized inverses, the perfect control procedure guarantees the structural stability behavior even for unstable systems. Thus, the notion of the nonminimum-phase property should be understood in terms of a possible achieveability covering the entire class of LTI MIMO continuous-time plants. Theoretical and practical simulation examples performed in the Matlab/Simulink environment confirm the feasibility of the newly introduced approach.

Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables.

In industry, ergonomists apply heuristic methods to determine workers' exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers' posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.

Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

Human-centered artificial intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities, or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, the interclass and intraclass spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model, which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as their transitioning over time from one state to another. Then, the assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated for both recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human-Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.

Erratum: Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

[This corrects the article DOI: 10.3389/frobt.2020.00080.].

Poincaré analysis of the electroencephalogram during sevoflurane anesthesia.

OBJECTIVE: The Poincaré plot is a two-dimensional state-space approach, where a timed signal is plotted against itself after a time delay, enabling determination of the dynamic nature of signals. Quantification of the Poincaré plot is a candidate for estimating anesthesia-dependent changes in the electroencephalogram (EEG). METHODS: In 20 patients, at four different states of anesthesia (0.5%, 1%, 2% and 3% sevoflurane), frontal EEG signals (10s) were used to construct Poincaré plots. The plot pattern was quantified by the standard deviation of the voltage dispersion along the line of identity (SD2), the standard deviation perpendicular to the line of identity (SD1) and their ratio (SD1/SD2), and compared using spectral EEG features. RESULTS: A significant stepwise decrease in the SD1/SD2 ratio was observed with each stepwise increase in sevoflurane concentration (p<0.001 for each). From 0.5% to 3% sevoflurane anesthesia, the ratio of relative ß power to δ power (ß/δ) was highly correlated with SD1/SD2 (R=0.92). CONCLUSIONS: The Poincaré plot of the frontal EEG can detect the significant changes in the depth of anesthesia induced by different sevoflurane concentrations. SIGNIFICANCE: The Poincaré plot is a useful technique for detecting the EEG changes induced by anesthesia.

Active integrated filters for RF-photonic channelizers.

A theoretical study of RF-photonic channelizers using four architectures formed by active integrated filters with tunable gains is presented. The integrated filters are enabled by two- and four-port nano-photonic couplers (NPCs). Lossless and three individual manufacturing cases with high transmission, high reflection, and symmetric couplers are assumed in the work. NPCs behavior is dependent upon the phenomenon of frustrated total internal reflection. Experimentally, photonic channelizers are fabricated in one single semiconductor chip on multi-quantum well epitaxial InP wafers using conventional microelectronics processing techniques. A state space modeling approach is used to derive the transfer functions and analyze the stability of these filters. The ability of adapting using the gains is demonstrated. Our simulation results indicate that the characteristic bandpass and notch filter responses of each structure are the basis of channelizer architectures, and optical gain may be used to adjust filter parameters to obtain a desired frequency magnitude response, especially in the range of 1-5 GHz for the chip with a coupler separation of ∼9 mm. Preliminarily, the measurement of spectral response shows enhancement of quality factor by using higher optical gains. The present compact active filters on an InP-based integrated photonic circuit hold the potential for a variety of channelizer applications. Compared to a pure RF channelizer, photonic channelizers may perform both channelization and down-conversion in an optical domain.