Hybrid Decoders for Marked Point Process Observations and External Influences.

OBJECTIVE: Internal physiological processes govern multiple state variables within the human body. Estimating these from point process-type bioelectric and biochemical observations is a challenge. Here we seek to estimate cortisol-related energy production and sympathetic arousal based on point process and continuous-valued data while permitting an external influence to affect the state estimates. METHODS: Traditional point process state-space methods, such as those used for estimating the aforementioned quantities from cortisol and skin conductance measurements respectively, suffer from the inability to permit the state estimates to also fit to an external influence (e.g. labels) or be guided by it. Here we modify an existing recurrent neural network (RNN) approach for state-space estimation through a weighted cost-function to enable a hybrid estimator that has this capability. RESULTS: Results on cortisol data based on a hypothetical sleep-wake influence term show how energy production can be estimated by permitting the estimates to fit to the external influence as much as desired. We further show how overfitting may be reduced by using circadian rhythm-based influence terms. Results on skin conductance data also indicate how the method can be used to estimate sympathetic arousal in an experiment containing stressors and relaxation, and permit an external influence as well. CONCLUSION: The RNN-based hybrid method is thus able to recover internal physiological states from point process and continuous-valued observations while permitting an external influence to guide the estimates. SIGNIFICANCE: The hybrid estimator could be embedded within wearable monitors that can be tailored based on domain expertise or individual feedback.

A Roadmap Towards Standards for Neurally Controlled End Effectors.

The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning.

Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.

A Bayesian Filtering Approach for Tracking Arousal From Binary and Continuous Skin Conductance Features.

OBJECTIVE: Neuroanatomical structures within the cortical and sub-cortical brain regions process emotion and cause subsequent variations in signals such as skin conductance and electrocardiography. The signals often encode information in their continuous-valued amplitudes or waves as well as in their underlying impulsive events. We propose to track psychological arousal from this hybrid source of skin conductance information. METHODS: We present a point process state-space method in tandem with Bayesian filtering for determining a continuous-valued arousal state from skin conductance measurements. To perform state estimation, we relate arousal to binary- and continuous-valued observations derived from the phasic and tonic parts of a skin conductance signal, and recover model parameters using expectation-maximization. We evaluate our model on both synthetic and two different experimental data sets. Stress was artificially induced in the first experimental data set and the second comprised of a fear conditioning experiment. RESULTS: Results on the first data set indicate high levels of arousal during exposure to cognitive stress and low arousal during relaxation. Plausible results are also obtained in the fear conditioning data set consistent with previous skin conductance studies in similar experimental contexts. CONCLUSION: The state-space approach-which does not rely on external classification labels-is able to continuously track an arousal level from skin conductance features. SIGNIFICANCE: The method is a promising arousal estimation scheme utilizing only skin conductance. The approach could find applications in wearable monitoring and the study of neuropsychiatric conditions such as post-traumatic stress disorder.

Skin Conductance as a Viable Alternative for Closing the Deep Brain Stimulation Loop in Neuropsychiatric Disorders.

Markers from local field potentials, neurochemicals, skin conductance, and hormone concentrations have been proposed as a means of closing the loop in Deep Brain Stimulation (DBS) therapy for treating neuropsychiatric and movement disorders. Developing a closed-loop DBS controller based on peripheral signals would require: (i) the recovery of a biomarker from the source neural stimuli underlying the peripheral signal variations; (ii) the estimation of an unobserved brain or central nervous system related state variable from the biomarker. The state variable is application-specific. It is emotion-related in the case of depression or post-traumatic stress disorder, and movement-related for Parkinson's or essential tremor. We present a method for closing the DBS loop in neuropsychiatric disorders based on the estimation of sympathetic arousal from skin conductance measurements. We deconvolve skin conductance via an optimization formulation utilizing sparse recovery and obtain neural impulses from sympathetic nerve fibers stimulating the sweat glands. We perform this deconvolution via a two-step coordinate descent procedure that recovers the sparse neural stimuli and estimates physiological system parameters simultaneously. We next relate an unobserved sympathetic arousal state to the probability that these neural impulses occur and use Bayesian filtering within an Expectation-Maximization framework for estimation. We evaluate our method on a publicly available data-set examining the effect of different types of stress on peripheral signal changes including body temperature, skin conductance and heart rate. A high degree of arousal is estimated during cognitive tasks, as are much lower levels during relaxation. The results demonstrate the ability to decode psychological arousal from neural activity underlying skin conductance signal variations. The complete pipeline from recovering neural stimuli to decoding an emotion-related brain state using skin conductance presents a promising methodology for the ultimate realization of a closed-loop DBS controller. Closed-loop DBS treatment would additionally help reduce unnecessary power consumption and improve therapeutic gains.

A Cortisol-Based Energy Decoder for Investigation of Fatigue in Hypercortisolism.

Hormones play a fundamental role in homeostasis. We develop a state-space model relating the body's internal energy to cortisol hormone secretions. Cortisol is secreted in pulses and follows a 24 h circadian rhythm. Secretory event timings carry important information regarding internal feedback signaling taking place, as do the upper and lower serum cortisol levels. We relate an internal energy state variable to cortisol pulse timings and to the upper and lower serum cortisol envelopes. We derive Bayesian filter equations for state estimation and use the Expectation-Maximization algorithm for model parameter recovery. Results on multi-day simulated data show circadian energy variations in healthy subjects and non-circadian fluctuations throughout 24 h periods in patient models suffering from hypercortisolism. The results shed new light on why patients diagnosed with excess cortisol disorders frequently experience symptoms of daytime fatigue and sleep disturbances at night. The state-space model is also an important first step towards the design of closed-loop controllers for treating hormone-related disorders in a manner that closely emulates the body's own pulsatile feedback mechanisms.

A Novel Filter for Tracking Real-World Cognitive Stress using Multi-Time-Scale Point Process Observations.

Determining the relationship between neurocognitive stress and changes in physiological signals is an important aspect of wearable monitoring. We present a state-space approach for tracking stress from skin conductance and electrocardiography measurements. Individual skin conductance responses (SCRs) are a primary source of information in a skin conductance signal and their rate of occurrence is related to psychological arousal. Likewise, heart rate too varies with emotion. We model SCRs and heartbeats as two different stress-related point processes linked to the same sympathetic nervous system activation. We derive Kalman-like filter equations for tracking stress and use both expectation-maximization and maximum likelihood estimation for parameter recovery. Our preliminary results show that stress is high when a task is unfamiliar, but reduces gradually with familiarity, albeit in the presence of other external stressors. The method demonstrates the feasibility of tracking real-world stress using skin conductance and heart rate measurements. It also serves as a novel state estimation framework for multiple point process observations on different time scales.

State-Space Modeling and Fuzzy Feedback Control of Cognitive Stress.

"Distress" or a substantial amount of stress may decrease brain functionality and cause neurological disorders. On the other hand, very low cognitive arousal may affect one's concentration and awareness. Data collected using wrist-worn wearable devices, in particular, skin conductance data, could be used to look into one's cognitive-stress-related arousal. Our goal here is to present excitatory and inhibitory wearable machine-interface (WMI) architectures to control one's cognitive-stress-related arousal state. We first present a model for skin conductance response events as a function of environmental stimuli associated with cognitive stress and relaxation. Then, we perform Bayesian filtering to estimate the hidden cognitive-stress-related arousal state. We finally close the loop using fuzzy control. In particular, we design two classes of controllers for our WMI architectures: (1) an inhibitory controller for reducing arousal and (2) an excitatory controller for increasing arousal. Our results illustrate that our simulated skin conductance responses are in agreement with experimental data. Moreover, we illustrate that our fuzzy control can successfully have both inhibitory and excitatory effects and regulate one's cognitive stress. In conclusion, in a simulation study based on experimental data, we have illustrated the feasibility of designing both excitatory and inhibitory WMI architectures. Since wearable devices can be used conveniently in one's daily life, the WMI architectures have a great potential to regulate one's cognitive stress seamlessly in real-world situations.

A State-Space Approach for Detecting Stress from Electrodermal Activity.

The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Increases in heart rate, salivary cortisol and skin conductance level are often observed accompanying high levels of stress. Stress can also take on different forms including emotional, cognitive and motivational. While a precise definition for stress is lacking, a pertinent issue is to quantify the state of psychological stress manifested in the nervous system. State-space models have previously been applied to estimate an unobserved neural state (e.g. learning, consciousness) from physiological signal measurements and data collected during behavioral experiments. In this paper, we relate stress to the probability that a phasic driver impulse occurs in skin conductance signals. We apply state-space modeling to extracted binary measures to continuously track a stress level across episodes of cognitive and emotional stress as well as relaxation. Results demonstrate a promising approach for tracking stress through wearable devices.