Numerical demonstration of low-reflective wire grid polarizers with a patterned FeO absorptive layer.

Absorptive polarizers are pivotal components for realizing a low ambient reflection in liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs). Different types of absorptive polarizers have been proposed. Nevertheless, the realization of compact and efficient absorptive polarizers remains challenging.

Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography.

Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia.

A Robust Extraction Approach of Auditory Brainstem Response Using Adaptive Kalman Filtering Method.

Objective: The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters.

Cryogenic temperature sensing based on the temperature dependence of color centers in optical fibers.

A cryogenic temperature sensor based on the temperature dependence of stable color centers in a commercial single-mode optical fiber is proposed. The radiation induced attenuation spectra at different temperatures are measured and decomposed by Ge-NBOHC and Ge(X) color centers. The configurational coordinate model is used to explain the temperature properties of the color centers.

Towards effective assessment of normal hearing function from ABR using a time-variant sweep-tone stimulus approach.

. The auditory brainstem response (ABR) audiometry is a means of assessing the functional status of the auditory neural pathway in the clinic. The conventional click ABR test lacks good neural synchrony and it mainly evaluates high-frequency hearing while the common tone-burst ABR test only detects hearing loss of a certain frequency at a time.

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography.

. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system.

Time-frequency Analysis of Electroencephalogram Signals in a Cognitive Decision-making Task.

Choices and decisions involve a series of complex cognitive processes, and the time-frequency analysis of electroencephalogram (EEG) signals can help understand the brain activities in different cognitive tasks. In this study, a decision-making cognitive task of rock-paper-scissors was designed, and the complex decision-making task was divided into three stages (decision planning, confirmation, and feedback). 64 channels of EEG signals were simultaneously recorded using the Neuroscan QuikCap system during the whole task.

The Effects of Channel Number on Classification Performance for sEMG-based Speech Recognition.

Speech recognition based on surface electromyography (sEMG) signals is an important research direction with potential applications in life, work and clinical. The number and placement of sEMG electrodes play a critical role in capturing the underlying sEMG activities and in turn, accurately classifying the speaking tasks. The aim of this work was to investigate the effect of the number of channels in speech recognition based on high-density (HD) sEMG.

The Effects of Random Stimulation Rate on Measurements of Auditory Brainstem Response.

Electroencephalography (EEG) signal is an electrophysiological recording from electrodes placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem response (ABR) is one type of EEG signals in response to an auditory stimulus, and it has been widely used to evaluate the potential disorders of the auditory function within the brain. Currently, the ABR measurements in the clinic usually adopt a fixed stimulation rate (FSR) technique in which the late evoked response could contaminate the ABR signals and deteriorate the waveform differentiation after averaging, thus compromising the overall auditory function assessment task.

A wearable 12-lead ECG acquisition system with fabric electrodes.

Continuous electrocardiogram (ECG) monitoring is significant for prevention of heart disease and is becoming an important part of personal and family health care. In most of the existing wearable solutions, conventional metal sensors and corresponding chips are simply integrated into clothes and usually could only collect few leads of ECG signals that could not provide enough information for diagnosis of cardiac diseases such as arrhythmia and myocardial ischemia. In this study, a wearable 12-lead ECG acquisition system with fabric electrodes was developed and could simultaneously process 12 leads of ECG signals.

Using spatial features for classification of combined motions based on common spatial pattern.

Motion recognition is an important application of electromyography (EMG) analysis. While discrete motions such as hand open, hand close and wrist pronation have been extensively investigated, studies on combined motions involving two or more degrees of freedom (DOFs) are relatively few and the classification accuracy of the combined motions reported in previous studies is barely satisfactory. To improve the accuracy of the combined motion recognition, common spatial pattern (CSP) was employed in this study to extract spatial features.

Electromyogram-based method to secure wireless body sensor networks for rehabilitation systems.

Wireless body sensor networks (WBSNs) provide a platform to track and monitor human health status as well as feedback to the user by capturing and processing certain physiological signals. Since WBSNs need to provide efficient health information privacy, their security has been identified as one of the major challenges, especially for rehabilitation systems. Conventionally, the random numbers (RNs) based on the inter-pulse intervals (IPIs) from electrocardiogram (ECG) recordings have been widely used to secure the data in WBSNs.

Towards reducing the impacts of unwanted movements on identification of motion intentions.

Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One such issue would be the effect of unwanted movements (UMs) that are inadvertently done by users on the performance of movement classification in EMG pattern recognition based algorithms.

Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method.

The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments.

Effects of non-training movements on the performance of motion classification in electromyography pattern recognition.

In electromyography pattern-recognition-based control of a multifunctional prosthesis, it would be inevitable for the users to unintentionally perform some classes of movements that are excluded from the training motion classes of a classifier, which might decay the performance of a trained classifier. It remains unknown how these untrained movements, designated as non-target movements (NTMs) in the study, would affect the performance of a trained classifier in the control of multifunctional prostheses. The goal of the current study was to evaluate the effects of NTMs on the performance of movement classification.

An in-situ calibration method and the effects on stimulus frequency otoacoustic emissions.

Background: The interference between the incoming sound wave and the acoustic energy reflected by the tympanic membrane (TM) forms a standing wave in human ear canals. The existence of standing waves causes various problems when measuring otoacoustic emissions (OAEs) that are soft sounds closely related with the functional status of the inner ear. The purpose of this study was to propose an in-situ calibration method to overcome the standing-wave problem and to improve the accuracy of OAE measurements.

Bioelectric signal detrending using smoothness prior approach.

Bioelectric signals such as electromyogram (EMG) and electrocardiogram (ECG) are often affected by various low-frequency trending interferences. It is critical to remove these interferences from the recordings so that the critical features of the bioelectric signals could be clearly observed. In this study, an advanced method based on smoothness prior approach (SPA) was proposed to solve this problem.

An adaptation strategy of using LDA classifier for EMG pattern recognition.

The time-varying character of myoelectric signal usually causes a low classification accuracy in traditional supervised pattern recognition method. In this work, an unsupervised adaptation strategy of linear discriminant analysis (ALDA) based on probability weighting and cycle substitution was suggested in order to improve the performance of electromyography (EMG)-based motion classification in multifunctional myoelectric prostheses control in changing environment. The adaptation procedure was firstly introduced, and then the proposed ALDA classifier was trained and tested with surface EMG recordings related to multiple motion patterns.

Decreased delta event-related synchronization in patients with early vascular dementia.

Electroencephalogram (EEG) activity, recorded while performing an "odd ball" detection task, was compared between patients with early vascular dementia (VD), healthy young controls and healthy elderly controls performing the same task. The data were analyzed using the event-related synchronization/desynchronization (ERS/ERD) method. VD patients, compared with controls, showed decreased ERS effects in the delta frequency band (0.