A Functional Electrical Stimulator to Enable Grasping Through Wrist Flexion

Functional electrical stimulation is an assistive technique that utilizes electrical discharges to produce functional movements in patients suffering from neurological impairments. In this work, a biphasic, programmable current- controlled functional electrical stimulator system is designed to enable hand grasping facilitated by wrist flexion. The developed system utilizes an operational amplifier based current source and is supported by a user interface to adjust stimulation parameters. The device is integrated with an accelerometer to measure the degree of stimulated movement. The system is validated, firstly, on two passive electrical loads and subsequently on four healthy volunteers. The device is designed to deliver currents between 0-30mA, and the error between the measured current and simulated current for two loads were -0.967±0.676mA and -0.995±0.97mA. The angular data from the accelerometer provided information regarding variations in movement between the subjects. The architecture of the proposed system is such that it can, in principle, automatically adjust the parameters of simulation to induce the desired movement optimally by measuring a stimulated movement artifact (e.g., angular position) in real time.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye-movements and muscle artifacts from electroencephalography (EEG). Due to other possible EEG contaminations, a rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. Noteworthy, as also revealed by our study, experts’ opinion about the nature of a component often disagrees highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on estimation of IC time-series, IC amplitude topography and spectral power distribution. The platform allows supervised ML model training and re-training on available data subsamples for better performance in specific tasks (i.e. movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model shows that it can be used not only for detection of noisy IC but also for automatic identifications of components related to the functional brain oscillations such as alpha and mu-rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will be used for continuous improvement of ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

Diastolic stress testing: similarities and differences between isometric handgrip and cycle echocardiography

Cycle echocardiography (CE) is recommended for noninvasive diagnosis of diastolic dysfunction but can be limited by respiratory and movement artifact. Isometric handgrip echocardiography (IHE) is also a robust diastolic discriminator, while avoiding the limitations associated with dynamic exercise. This study sought to compare these two diastolic stress testing approaches. Twelve elderly individuals were recruited from the community (age 71 ± 6 yr). Heart rate, arterial blood pressure, and left ventricular (LV) diastolic function (via echocardiography) were assessed at rest and in response to 3 min of IHE at 40% of their maximal voluntary contraction, followed by 3 min of CE at 20 W. Both IHE and CE caused a significant increase in heart rate and blood pressure, leading to similar increases in myocardial oxygen demand. Both stressors also evoked a similar rise in the ratio between early LV mitral inflow velocity to early lateral annular velocity, a surrogate measure of LV filling pressure. The underlying mechanisms leading to these changes, however, were inherently different. IHE increased mean arterial pressure, and impaired myocardial relaxation, to a greater extent than CE. In contrast, CE augmented cardiac index, and increased early mitral filling velocity, to a great extent than IHE. In conclusion, for the first time, these data highlight several important similarities and differences between IHE and CE. That IHE avoids respiratory and movement artifact, while still serving as a robust diastolic discriminator, supports IHE as a strong alternative to CE for diastolic stress testing. NEW & NOTEWORTHY This is the first study to compare the diastolic stress response between isometric handgrip exercise and conventional cycle exercise. The data suggest that isometric handgrip echocardiography is comparable to conventional cycle echocardiography, both in terms of its hemodynamic challenge and global diastolic stress response. That isometric handgrip echocardiography eliminates both respiratory and movement artifact and is low cost and incredibly portable supports its integration into routine echocardiography exams.