Seizure forecasting with ultra long-term EEG signals.

Objective: The apparent randomness of seizure occurrence affects greatly the quality of life of persons with epilepsy. Since seizures are often phase-locked to multidien cycles of interictal epileptiform activity, a recent forecasting scheme, exploiting RNS data, is capable of forecasting seizures days in advance.

Methods: We tested the use of a bandpass filter to capture the universal mid-term dynamics enabling both patient-specific and cross-patient forecasting.

An analytical approach to engineer multistability in the oscillatory response of a pulse-driven ReRAM.

A nonlinear system, exhibiting a unique asymptotic behaviour, while being continuously subject to a stimulus from a certain class, is said to suffer from fading memory. This interesting phenomenon was first uncovered in a non-volatile tantalum oxide-based memristor from Hewlett Packard Labs back in 2016 out of a deep numerical investigation of a predictive mathematical description, known as the Strachan model, later corroborated by experimental validation. It was then found out that fading memory is ubiquitous in non-volatile resistance switching memories.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems.

Infrared Thermographic Imaging of Chest Wall Perfusion in Patients Undergoing Coronary Artery Bypass Grafting.

Coronary artery disease represents a leading cause of death worldwide, to which the coronary artery bypass graft (CABG) is the main method of treatment in advanced multiple vessel disease. The use of the internal mammary artery (IMA) as a graft insures an improved long-term survival, but impairment of chest wall perfusion often leads to surgical site infection and increased morbidity and mortality. Infrared thermography (IRT) has established itself in the past decades as a non-invasive diagnostic technique.

Registration of IRT and visible light images in neurosurgery: analysis and comparison of automatic intensity-based registration approaches.

Purpose: The purpose of this study is to analyze and compare six automatic intensity-based registration methods for intraoperative infrared thermography (IRT) and visible light imaging (VIS/RGB). The practical requirement is to get a good performance of Euclidean distance between manually set landmarks in reference and target images as well as to achieve a high structural similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR) with respect to the reference image.

Methods: In this study, preprocessing is applied to bring both image types to a similar intensity.

Coherent false seizure prediction in epilepsy, coincidence or providence?

Objective: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data.

Methods: We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods.

Selective and self-validating breath-level detection of hydrogen sulfide in humid air by gold nanoparticle-functionalized nanotube arrays.

Unlabelled: We demonstrate the selective detection of hydrogen sulfide at breath concentration levels under humid airflow, using a self-validating 64-channel sensor array based on semiconducting single-walled carbon nanotubes (sc-SWCNTs). The reproducible sensor fabrication process is based on a multiplexed and controlled dielectrophoretic deposition of sc-SWCNTs. The sensing area is functionalized with gold nanoparticles to address the detection at room temperature by exploiting the affinity between gold and sulfur atoms of the gas.

On Local Activity and Edge of Chaos in a NaMLab Memristor.

Local activity is the capability of a system to amplify infinitesimal fluctuations in energy. Complex phenomena, including the generation of action potentials in neuronal axon membranes, may never emerge in an open system unless some of its constitutive elements operate in a locally active regime. As a result, the recent discovery of solid-state volatile memory devices, which, biased through appropriate DC sources, may enter a local activity domain, and, most importantly, the associated stable yet excitable sub-domain, referred to as edge of chaos, which is where the seed of complexity is actually planted, is of great appeal to the neuromorphic engineering community.

Motion Correction in Multimodal Intraoperative Imaging.

Thermographic imaging accompanied with time-resolved analysis is a promising technique for intraoperative imaging in neurosurgery. However, motion due to breathing and pulse of the patient introduces large inaccuracies to the demarcation of normal and pathological brain tissue. Since movements and physiological processes are both manifested as temperature variations, we employ co-registered visual-light images to unambiguously detect motion.

Intraoperative motion correction in neurosurgery: a comparison of intensity- and feature-based methods.

The intraoperative identification of normal and anomalous brain tissue can be disturbed by pulsatile brain motion and movements of the patient and surgery devices. The performance of four motion correction methods are compared in this paper: Two intensity-based, applying optical flow algorithms, and two feature-based, which take corner features into account to track brain motion. The target registration error with manually selected marking points and the temporal standard deviation of intensity were analyzed in the evaluation.

Registration and Fusion of Thermographic and Visual-Light Images in Neurosurgery.

An intraoperative imaging system facilitating the localisation and characterisation of functional areas, pathological tissue, or perfusion disorders, could enormously support medical decisions during neurosurgical interventions and, thus, reduce the risk for the patients. To provide both structural and functional information of the brain tissue to the surgeon, a novel multimodal approach based on the measurement of long-wave infrared radiation and visual-light imaging is very promising. In this contribution, we discuss various methods for the registration and fusion of thermographic and visual-light images.

Neuronal synapse as a memristor: modeling pair- and triplet-based STDP rule.

We propose a new memristive model for the neuronal synapse based on the spike-timing-dependent plasticity (STDP) protocol, considering both long-term and short-term plasticity in the synapse. Higher-order behavior is modeled by a memristor with adaptive thresholds, which realizes the well-established suppression principle of Froemke. We assume a mechanism of variable thresholds adapting to synaptic potentiation (LTP) and depression (LTD), which reproduces the refractory time in the weight modification.

Adaptive Neuromorphic Architecture (ANA).

We designed Adaptive Neuromorphic Architecture (ANA) that self-adjusts its inherent parameters (for instance, the resonant frequency) naturally following the stimuli frequency. Such an architecture is required for brain-like engineered systems because some parameters of the stimuli (for instance, the stimuli frequency) are not known in advance. Such adaptivity comes from a circuit element with memory, namely mem-inductor or mem-capacitor (memristor's sisters), which is history-dependent in its behavior.

Analysis of multidimensional neural activity via CNN-UM.

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.