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Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications. [ABSTRACT FROM AUTHOR]

Traditional Potentiometric Ion-selective Electrodes (ISE) are widely used in industrial and clinical settings. The simplicity and small footprint of ISE have encouraged their recent adoption as wearable/implantable sensors for personalized healthcare and precision agriculture, creating a new set of unique challenges absent in traditional ISE. In this paper, we develop a fundamental physics-based model to describe both steady-state and transient responses of ISE relevant for wearable/implantable sensors. The model is encapsulated in a “generalized Nernst formula” that explicitly accounts for the analyte density, time-dynamics of signal transduction, ion-selective membrane thickness, and other sensor parameters. The formula is validated numerically by self-consistent modeling of multispecies ion-transport and experimentally by interpreting the time dynamics and thickness dependence of thin-film solid-contact and graphene-based ISE sensors for measuring soil nitrate concentration. These fundamental results will support the accelerated development of ISE for wearable/implantable applications. [ABSTRACT FROM AUTHOR]

In this article, we demonstrate a wireless and passive physiological pressure sensing scheme that utilizes ultrasound imaging of an implantable microfluidic based pressure sensitive transducer. The transducer consists of a sub-mm scale pressure sensitive membrane that covers a reservoir filled with water and is connected to a hydrophobic micro-channel. Applied pressure onto the transducer deflects the membrane and pushes the water from the reservoir into the channel; the water's travelling distance in the channel is a function of the applied pressure, which is quantitatively measured by using a 40 MHz ultrasound imaging system. The sensor presents a linear sensitivity of 42 kPa/mm and a spatial resolution of 1.2 kPa/30 μm in the physiological range of abdominal compartment syndrome. Reliability assessments of the transducer confirm its ability to remain functional after more than 600 cycles of pressure up to 55 kPa over the course of 2 days. Ex vivo experimental results verify the practical capability of the technology to effectively measure pressures under a 15 mm thick porcine skin. It is anticipated that this technology can be applied to a broad range of implantable pressure measurement, by simply tuning the thickness of the thin polydimethylsiloxane membrane and the geometry of the reservoir. [ABSTRACT FROM AUTHOR]

Objective: Brain network connectivity analysis plays an important role in computer-aided automatic localization of seizure onset zone (SOZ) from Intracranial Electroencephalography (iEEG). However, how to accurately compute brain network dynamics is still not well addressed. This work aims to develop an effective measure to find out the dynamics for SOZ localization. Methods: Given multiple-channel iEEG signals, the ictal process involves continuous changes of information propagation. In each time slot, the connectivity relationship between channels can be represented as a matrix. Since the matrices from different time slots do not lie on vector spaces, the similarity between them cannot be computed directly. In this paper, we regard the matrices as points on a Riemannian manifold, so that the similarity can be measured by the geodesic distance on the manifold. It addresses the information-losing problem in existing methods using a vector to approximate a matrix. With the Riemannian method, the brain network dynamics are figured out by clustering methods. A temporal segmentation process is applied to refine the segments for SOZ localization. Results: Our method is evaluated on six epilepsy patients, and the SOZ localization performance is evaluated by the area under the curve (AUC) score. Overall, our method obtains an average AUC score of 0.875, which outperforms the existing approaches. Conclusion: Our method preserves more information in measuring the relationship between brain connectivity descriptors, thus is more robust for SOZ localization. Significance: Our method has great potentials for clinical epilepsy treatments. [ABSTRACT FROM AUTHOR]

In this paper, we present a smart capsule that can release its payload after a predetermined/adjustable delay subsequent to passing from the stomach into the small intestine. The described capsule (9 mm × 22 mm) comprises a pH-sensitive hydrogel-based switch, an electronic compartment containing a capacitor charged to 2.7 V, and a drug reservoir capped by a taut fusible thread intertwined with a nichrome wire. The nichrome wire, capacitor, and pH-responsive electrical switch are connected in series. The pH transition the capsule encounters when it enters the small intestine triggers controlled swelling of the pH-responsive hydrogel, which pushes a conductive elastic membrane to close an electrical switch. This initiates a sequence of events, i.e., the discharge of the capacitor, heating the nichrome wire, breakage of the fusible thread, and release of the payload stored in the capsule reservoir through the unlatched cap. The time lag between initiation of hydrogel swelling (by the near-neutral pH of the small intestine) and payload release is controlled by the deflection of the conductive elastic membrane and the gap separating the contacts. The release time can be set to within ±5 min after one hour in the small intestine (start of the swelling) increasing to ±40 min after 4 h. [ABSTRACT FROM AUTHOR]