Accelerating the phase demodulation process for heterodyne Φ-OTDR using spatial phase shifting.

An effective orthogonal signal generation method for heterodyne-detection-based phase-sensitive optical time-domain reflectometer systems is proposed to accelerate the phase demodulation process. The demodulation principle is based on the spatial phase shifting technique. By exploiting the relative phase difference between adjacent spatial sampling channels, the orthogonal signal is easily obtained from basic algebra calculations.

Double-Sided Sapphire Optrodes with Conductive Shielding Layers to Reduce Optogenetic Stimulation Artifacts.

Optrodes, which are single shaft neural probes integrated with microelectrodes and optical light sources, offer a remarkable opportunity to simultaneously record and modulate neural activities using light within an animal's brain; however, a common problem with optrodes is that stimulation artifacts can be observed in the neural recordings of microelectrodes when the light source on the optrode is activated. These stimulation artifacts are undesirable contaminants, and they cause interpretation complexity when analyzing the recorded neural activities. In this paper, we tried to mitigate the effects of the stimulation artifacts by developing a low-noise, double-sided optrode integrated with multiple Electromagnetic Shielding (EMS) layers.

Adaptive Fiber-Ring Lasers Based on Isopropanol Filled Microfiber Coupler for High-Sensitivity Temperature Sensing.

We demonstrated a new method for temperature measurement inside a fiber ring laser (FRL) cavity. Different from traditional FRL temperature sensing system which need additional filter working as a sensor, a micro-fiber coupler (MFC) was designed as a beam splitter, filter, and temperature sensor. In addition, isopropanol, a liquid with very high photothermal coefficient, is selectively filled in the MFC in order to improve the sensitivity of the system on temperature.

An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features.

Emotion can be influenced during self-isolation, and to avoid severe mood swings, emotional regulation is meaningful. To achieve this, efficiently recognizing emotion is a vital step, which can be realized by electroencephalography signals. Previously, inspired by the knowledge of sequencing in bioinformatics, a method termed brain rhythm sequencing that analyzes electroencephalography as the sequence consisting of the dominant rhythm has been proposed for seizure detection.

Single-Channel Selection for EEG-Based Emotion Recognition Using Brain Rhythm Sequencing.

Recently, electroencephalography (EEG) signals have shown great potential for emotion recognition. Nevertheless, multichannel EEG recordings lead to redundant data, computational burden, and hardware complexity. Hence, efficient channel selection, especially single-channel selection, is vital.

EEG-based Emotion Recognition Using Similarity Measure of Brain Rhythm Sequencing.

The similarity is a fundamental measure from the homology theory in bioinformatics, and the biological sequence can be classified based on it. However, such an approach has not been utilized for electroencephalography (EEG)-based emotion recognition. To this end, the sequence generated by choosing the dominant brain rhythm owning maximum instantaneous power at each 0.

Influence of the Surface Material and Illumination upon the Performance of a Microelectrode/Electrolyte Interface in Optogenetics.

Integrated optrodes for optogenetics have been becoming a significant tool in neuroscience through the combination of offering accurate stimulation to target cells and recording biological signals simultaneously. This makes it not just be widely used in neuroscience researches, but also have a great potential to be employed in future treatments in clinical neurological diseases. To optimize the integrated optrodes, this paper aimed to investigate the influence of surface material and illumination upon the performance of the microelectrode/electrolyte interface and build a corresponding evaluation system.

A Review on Analytical Modeling for Collapse Mode Capacitive Micromachined Ultrasonic Transducer of the Collapse Voltage and the Static Membrane Deflections.

Analytical modeling of capacitive micromachined ultrasonic transducer (CMUT) is one of the commonly used modeling methods and has the advantages of intuitive understanding of the physics of CMUTs and convergent when modeling of collapse mode CMUT. This review article summarizes analytical modeling of the collapse voltage and shows that the collapse voltage of a CMUT correlates with the effective gap height and the electrode area. There are analytical expressions for the collapse voltage.

Experimental Characterization of an Embossed Capacitive Micromachined Ultrasonic Transducer Cell.

Capacitive Micromachined Ultrasonic Transducer (CMUT) is a promising ultrasonic transducer in medical diagnosis and therapeutic applications that demand a high output pressure. The concept of a CMUT with an annular embossed pattern on a membrane working in collapse mode is proposed to further improve the output pressure. To evaluate the performance of an embossed CMUT cell, both the embossed and uniform membrane CMUT cells were fabricated in the same die with a customized six-mask sacrificial release process.

Low-latency single channel real-time neural spike sorting system based on template matching.

Recent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching.

Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs.

Objective: Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data.

Approach: A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli.

Electromagnetic Field Analysis of Low-Magnitude High-Frequency Vibrator with Multiple Plungers.

Low-magnitude high-frequency (LMHF) of vibrational stimulation has been accepted as an effective method to enhance bone remolding. However, the electromagnetic field (EMF) generated by the vibrator could also be an influence factor in the vibrational experiments. This phenomenon underlies the bone remodeling effect caused by vibrational stimulation is disrupted to be investigated.

Monolithic Multiband CMUTs for Photoacoustic Computed Tomography With In Vivo Biological Tissue Imaging.

Among the biomedical imaging modalities, photoacoustic computed tomography (PACT) was one of the emerging hybrid techniques in recent years. In designing the PACT imaging system, a finite-bandwidth transducer is one of the limited factors for the overall performance. As the target size is inversely proportional to the dominant frequency components of the generated photoacoustic (PA) signal, a broad bandwidth transducer is desired for different scales' imaging.

Development of a multi-band photoacoustic tomography imaging system based on a capacitive micromachined ultrasonic transducer array.

Photoacoustic tomography (PAT) as a hybrid technology combines the high optical contrast and high acoustic resolution in a single imaging modality. However, most of the available PAT systems cannot comprehensively or accurately characterize biological systems at multiple length scales due to the use of narrow bandwidth commercial ultrasonic transducers. In this study, we fabricated a novel multi-band capacitive micromachined ultrasonic transducer (CMUT) array, and first developed a CMUT-based multi-band photoacoustic tomography (MBPAT) imaging system.

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

Objective: The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics.

Methods: A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC.

Development of a Calibration Strip for Immunochromatographic Assay Detection Systems.

With many benefits and applications, immunochromatographic (ICG) assay detection systems have been reported on a great deal. However, the existing research mainly focuses on increasing the dynamic detection range or application fields. Calibration of the detection system, which has a great influence on the detection accuracy, has not been addressed properly.

Measurement and analysis of channel attenuation characteristics for an implantable galvanic coupling human-body communication.

In this study, an experiment was designed to verify the low power consumption of galvanic coupling human-body communication. A silver electrode (silver content: 99%) is placed in a pig leg and a sine wave signal with the power of 0 dBm is input. Compared with radio frequency communication and antenna transmission communication, attenuation is reduced by approximately 10 to 15 dB, so channel characteristics are highly improved.

Three-Dimensional Segmentation and Quantitative Measurement of the Aqueous Outflow System of Intact Mouse Eyes Based on Spectral Two-Photon Microscopy Techniques.

Purpose: To visualize and quantify the three-dimensional (3D) spatial relationships of the structures of the aqueous outflow system (AOS) within intact enucleated mouse eyes using spectral two-photon microscopy (TPM) techniques.

Methods: Spectral TPM, including two-photon autofluorescence (TPAF) and second-harmonic generation (SHG), were used to image the small structures of the AOS within the limbal region of freshly enucleated mouse eyes. Long infrared excitation wavelengths (930 nm) were used to reduce optical scattering and autofluorescent background.

Communication channel modeling of human forearm with muscle fiber tissue characteristics.

Human-Body Communication (HBC) is a wireless communication method using the human body tissue as a transmission medium for signals. This paper on the basis of human muscle fiber tissues' characteristics, it is first proposed to establish the analytical model of galvanic coupling human-body communication channel. In this model, the parallel and the transverse electrical characteristics of muscular tissue are fully considered, and the model accurately presents the transmission mechanism of galvanic coupling human-body communication signals in the channel.

Effects of human limb gestures on galvanic coupling intra-body communication for advanced healthcare system.

Background: Intra-Body Communication (IBC), which utilizes the human body as the transmission medium to transmit signal, is a potential communication technique for the physiological data transfer among the sensors of remote healthcare monitoring system, in which the doctors are permitted to remotely access the healthcare data without interrupt to the patients' daily activities.

Methods: This work investigates the effects of human limb gestures including various joint angles, hand gripping force and loading on galvanic coupling IBC channel. The experiment results show that channel gain is significantly influenced by the joint angle (i.

Design of a Collapse-Mode CMUT With an Embossed Membrane for Improving Output Pressure.

Capacitive micromachined ultrasonic transducers (CMUTs) have emerged as a competitive alternative to piezoelectric ultrasonic transducers, especially in medical ultrasound imaging and therapeutic ultrasound applications, which require high output pressure. However, as compared with piezoelectric ultrasonic transducers, the output pressure capability of CMUTs remains to be improved. In this paper, a novel structure is proposed by forming an embossed vibrating membrane on a CMUT cell operating in the collapse mode to increase the maximum output pressure.

A Novel Field-Circuit FEM Modeling and Channel Gain Estimation for Galvanic Coupling Real IBC Measurements.

Existing research on human channel modeling of galvanic coupling intra-body communication (IBC) is primarily focused on the human body itself. Although galvanic coupling IBC is less disturbed by external influences during signal transmission, there are inevitable factors in real measurement scenarios such as the parasitic impedance of electrodes, impedance matching of the transceiver, etc. which might lead to deviations between the human model and the in vivo measurements.

Beta/theta ratio neurofeedback training effects on the spectral topography of EEG.

Neurofeedback training (NFT) has shown positive effects on cognition and behavior enhancement as well as clinical treatment. However, little is known about the training effects in brain activity besides training location which is crucial for understanding the mechanism of neurofeedback and enhancing training efficiency. This study aimed to investigate beta/theta ratio (BTR) NFT effects on the spectral topography of electroencephalogram (EEG).

Circuit models and experimental noise measurements of micropipette amplifiers for extracellular neural recordings from live animals.

Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested.