The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses

A USCLIVAR Project to Assess and Compare the Responses of Global Climate Models to Drought-Related SST Forcing Patterns: Overview and Results

The USCLI VAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are the mechanisms that maintain drought across the seasonal cycle and from one year to the next? What is the role of the leading patterns of SST variability, and what are the physical mechanisms linking the remote SST forcing to regional drought, including the role of land-atmosphere coupling? The runs were carried out with five different atmospheric general circulation models (AGCM5), and one coupled atmosphere-ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Nino/Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic Multi-decadal Oscillation (AMO), and a global trend pattern. One of the key findings is that all the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the U.S. tends to occur when the two oceans have anomalies of opposite sign. That is, a cold Pacific and warm Atlantic tend to produce the largest precipitation reductions, whereas a warm Pacific and cold Atlantic tend to produce the greatest precipitation enhancements. Further analysis of the response over the U.S. to the Pacific forcing highlights a number of noteworthy and to some extent unexpected results. These include a seasonal dependence of the precipitation response that is characterized by signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. Another interesting result concerns what appears to be a substantially different character in the surface temperature response over the U.S. to the Pacific forcing by the only model examined here that was developed for use in numerical weather prediction. The response to the positive SST trend forcing pattern is an overall surface warming over the world's land areas with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all the models

Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated

Near-infrared Light Responsive Upconversion Nanoparticles for Imaging, Drug Delivery and Therapy of Cancers

Cancers have become serious threat to human health and life, and they are critical to develop safe and effective theranostic methods for diagnosis and therapy of tumors. In recent years, real time cancer theranostic visualization systems (RT-CTVS) based on light-responsive nanoparticles have been developed. Especially, upconversion nanoparticles (UCNPs) have excellent optical properties and unique near-infrared (NIR) responsive. The minimized photodamage, low autofluorescence and high penetration depth can be achieved with UCNPs. Therefore, UCNPs are widely used in real time NIR mediated visualization systems of cancer diagnosis and therapy. In this review, we focus on the latest developments of rare earth ions doped upconversion fluorescence nanoparticles. First, the synthesis methods of UCNPs were briefly introduced. Second, the strategies of UCNPs surface modifications, including the ligand exchange, ligand oxidation, ligand interaction, ligand free synthesis, layer by layer growth and surface silanization were summarized. Third, the recent research progress in applying UCNPs to construct NIR light stimuli-responsive RT-CTVS, including imaging, drug delivery and photodynamic therapy (PDT) were highlighted. Finally, some of the current problems and future effort directions in these fields were also proposed

Experiments and assessments of a 3-DOF haptic device for interactive operation

Abstract Haptic devices have been applied in interactive operation to perform contact tasks. To explore the haptic perception characteristics of typical push-pull and rotation operation, an experimental system was built by incorporating a three degrees of freedom (3-DOF) haptic device and the virtual environment. In this system, the haptic device is used to provide motion commands to control the avatar in the virtual environment and to exert haptic feedback on the human operator generated by three motors. In order to evaluate the main influential factors of interactive system based on haptic devices, ergonomic assessments are designed and experimentally implemented. Preliminary studies on the factors including restoring force, guidance force, speed of the virtual avatar, and the arm length have been conducted. The results are of great significance for the design of a haptic device and haptic interaction system by analyzing the specific requirements of ergonomics

The Effect of Hydrogen Addition on the Combustion Characteristics of RP-3 Kerosene/Air Premixed Flames

Experimental studies have been performed to investigate the effects of hydrogen addition on the combustion characteristics of Chinese No.3 jet fuel (RP-3 kerosene/air premixed flames. Experiments were carried out in a constant volume chamber and the influences of the initial temperatures of 390 and 420 K, initial pressures of 0.1 and 0.3 MPa, equivalence ratios of 0.6–1.6 and hydrogen additions of 0.0–0.5 on the laminar burning velocities, and Markstein numbers of Hydrogen (H2)/RP-3/air mixtures were investigated. The results show that the flame front surfaces of RP-3/air mixtures remain smooth throughout the entire flame propagation process at a temperature of 390 K, pressure of 0.3 MPa, equivalence ratio of 1.3 and without hydrogen addition, but when the hydrogen addition increases from 0.0 to 0.5 under the same conditions, flaws and protuberances occur at the flame surfaces. It was also found that with the increase of the equivalence ratio from 0.9 to 1.5, the laminar burning velocities of the mixtures increase at first and then decrease, and the highest laminar burning velocity was measured at an equivalence ratio of 1.2. Meanwhile, with the increase of hydrogen addition, laminar burning velocities of H2/RP-3/air mixtures increase. However, the Markstein numbers of H2/RP-3/air mixtures decrease with the increase of hydrogen addition, which means that the flames of H2/RP-3/air mixtures become unstable with the increase of hydrogen addition

Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials

The topdown determined visual object perception refers to the ability of a person to identify a prespecified visual target. This paper studies the technical foundation for measuring the target-perceptual ability in a guided visual search task, using the EEG-based brain imaging technique. Specifically, it focuses on the feature representation learning problem for single-trial classification of fixation-related potentials (FRPs). The existing methods either capture only first-order statistics while ignoring second-order statistics in data, or directly extract second-order statistics with covariance matrices estimated with raw FRPs that suffer from low signal-to-noise ratio. In this paper, we propose a new representation learning pipeline involving a low-level convolution subnetwork followed by a high-level Riemannian manifold subnetwork, with a novel midlevel pooling layer bridging them. In this way, the discriminative power of the first-order features can be increased by the convolution subnetwork, while the second-order information in the convolutional features could further be deeply learned with the subsequent Riemannian subnetwork. In particular, the temporal ordering of FRPs is well preserved for the components in our pipeline, which is considered to be a valuable source of discriminant information. The experimental results show that proposed approach leads to improved classification performance and robustness to lack of data over the state-of-the-art ones, thus making it appealing for practical applications in measuring the target-perceptual ability of cognitively impaired patients with the FRP technique

Cancer cell detection and imaging: MRI-SERS bimodal splat-shaped Fe3O4/Au nanocomposites

Cancer cell detection and imaging: MRI-SERS bimodal splat-shaped Fe3O4/Au nanocomposite