A Time-varying Equivalent Circuit Modeling and Measuring Approach for Intracardiac Communication in Leadless Pacemakers

Intracardiac wireless communication is crucial for the development of multi-chamber leadless cardiac pacemakers (LCP). However, the time-varying characteristics of intracardiac channel pose major challenges. As such, mastering the dynamic conduction properties of the intracardiac channel and modeling the equivalent time-varying channel are imperative for realizing LCP multi-chamber pacing. In this paper, we present a limiting volume variational approach based on the electrical properties of cardiac tissues and trends in chamber volume variation. This approach was used to establish a quasi-static and a continuous time-varying equivalent circuit model of an intracardiac channel. An equivalence analysis was conducted on the model, and a discrete time-varying equivalent circuit phantom grounded on the cardiac cycle was subsequently established. Moreover, an ex vivo cardiac experimental platform was developed for verification. Results indicate that in the frequency domain, the congruence between phantom and ex vivo experimental outcomes is as high as 94.3%, affirming the reliability of the equivalent circuit model. In the time domain, the correlation is up to 75.3%, corroborating its effectiveness. The proposed time-varying equivalent circuit model exhibits stable and standardized dynamic attributes, serving as a powerful tool for addressing time-varying challenges and simplifying in vivo or ex vivo experiments.

A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)-1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Modulating Individual Alpha Frequency through Short-Term Neurofeedback for Cognitive Enhancement in Healthy Young Adults

Human alpha oscillation (7-13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

Spiking neural networks (SNNs) are more energy- and resource-efficient than artificial neural networks (ANNs). However, supervised SNN learning is a challenging task due to non-differentiability of spikes and computation of complex terms. Moreover, the design of SNN learning engines is not an easy task due to limited hardware resources and tight energy constraints. In this article, a novel hardware-efficient SNN back-propagation scheme that offers fast convergence is proposed. The learning scheme does not require any complex operation such as error normalization and weight-threshold balancing, and can achieve an accuracy of around 97.5% on MNIST dataset using only 158,800 synapses. The multiplier-less inference engine trained using the proposed hard sigmoid SNN training (HaSiST) scheme can operate at a frequency of 135 MHz and consumes only 1.03 slice registers per synapse, 2.8 slice look-up tables, and can infer about 0.03[Formula: see text] features in a second, equivalent to 9.44 giga synaptic operations per second (GSOPS). The article also presents a high-speed, cost-efficient SNN training engine that consumes only 2.63 slice registers per synapse, 37.84 slice look-up tables per synapse, and can operate at a maximum computational frequency of around 50 MHz on a Virtex 6 FPGA.

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. The simulation and experimental results showed that the proposed method achieved >100% computation speed improvement compared with the conventional methods, with a slight trade-off in phase demodulation performance. Therefore, the proposed method is potentially beneficial for the distributed acoustic sensing technology for reducing the computation complexity of phase demodulation procedures.

Fiber Temperature Sensor Based on Vernier Effect and Optical Time Stretching Method

A novel method for ultra-sensitive and ultra-fast temperature sensing has been successfully implemented by cascading Saganc rings to generate the Vernier effect and doing the same dispersive fibers to achieve the optical time-stretching effect. This is different from the traditional point fiber sensor demodulated by optical spectrum analyzer (OSA) whose demodulation speed is usually at the second level. The designed system maps the wavelength domain to the time domain through the dispersive fiber, which can realize the ultra-fast temperature monitoring at the nanosecond level. The cascaded Sagnac ring is composed of polarization maintaining fiber (PMF) which is significantly affected by the thermal-optical coefficient. When the temperature changes, the variation is as high as -6.228 nm/°C, which is 8.5 times higher than the sensitivity based on the single Sagnac ring system. Furthermore, through the optical time stretching scheme, the corresponding response sensitivity is increased from 0.997 ns/°C to 7.333 ns/°C, and the magnification is increased 7.4 times with a response speed of 50 MHz.

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. The LED and microelectrodes were constructed separately on the top epitaxial and bottom substrate layers, and EMS layers were used to separate the microelectrodes and LED to reduce signal cross-talks. Compared with conventional single-sided designs, in which the LED and microelectrodes are constructed on the same side, our results indicate that double-sided optrodes can significantly reduce the presence of stimulation artifacts. In addition, the presence of stimulation artifacts can further be reduced by decreasing the voltage difference and increasing the rise/fall time of the driving LED pulsed voltage. With all these strategies, the presence of stimulation artifacts was significantly reduced by ~76%. As well as stimulation suppression, the sapphire substrate also provided strong mechanical stiffness and support to the optrodes, as well as improved electronic stability, thus making the double-sided sapphire optrodes highly suitable for optogenetic neuroscience research on animal models.

Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem

Spatial hearing allows animals to rapidly detect and localize auditory events in the surrounding environment. The auditory brainstem plays a central role in processing and extracting binaural spatial cues through microsecond-precise binaural integration, especially for detecting interaural time differences (ITDs) of low-frequency sounds at the medial superior olive (MSO). A series of mechanisms exist in the underlying neural circuits for preserving accurate action potential timing across multiple fibers, synapses and nuclei along this pathway. One of these is the myelination of afferent fibers that ensures reliable and temporally precise action potential propagation in the axon. There are several reports of fine-tuned myelination patterns in the MSO circuit, but how specifically myelination influences the precision of sound localization remains incompletely understood. Here we present a spiking neural network (SNN) model of the Mongolian gerbil auditory brainstem with myelinated axons to investigate whether different axon myelination thicknesses alter the sound localization process. Our model demonstrates that axon myelin thickness along the contralateral pathways can substantially modulate ITD detection. Furthermore, optimal ITD sensitivity is reached when the MSO receives contralateral inhibition via thicker myelinated axons compared to contralateral excitation, a result that is consistent with previously reported experimental observations. Our results suggest specific roles of axon myelination for extracting temporal dynamics in ITD decoding, especially in the pathway of the contralateral inhibition.

Quantitative demodulation of distributed low-frequency vibration based on phase-shifted dual-pulse phase-sensitive OTDR with direct detection

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been proposed for distributed vibration sensing purpose over recent years. Emerging applications, including seismic and hydroacoustic wave detection, demand accurate low-frequency vibration reconstruction capability. We propose to use the direct-detection Φ-OTDR configuration to achieve quantitative demodulation of external low-frequency vibrations by phase-shifted dual-pulse probes. Simultaneous pulsing and phase shifting modulation is realized with a single acousto-optic modulator to generate such probes, relaxing the need for an additional optical phase modulator. In the experiments, vibrations with frequency as low as 0.5 Hz are successfully reconstructed with 10 m spatial resolution and 35 dB signal-to-noise ratio. Excellent linearity and repeatability are demonstrated between the optical phase demodulation results and the applied vibration amplitudes. The proposed method is capable of quantitative demodulation of low-frequency vibrations with a cost-effective system configuration and high computation efficiency, showing potential for commercial applications of distributed seismic or hydroacoustic wave acquisition.

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. For this purpose, a technique termed brain rhythm sequencing (BRS) that interprets EEG based on a dominant brain rhythm having the maximum instantaneous power at each 0.2s timestamp has been proposed. Then, dynamic time warping (DTW) is used for rhythm sequence classification through the similarity measure. After evaluating the rhythm sequences for the emotion recognition task, the representative channel that produces impressive accuracy can be found, which realizes single-channel selection accordingly. In addition, the appropriate time segment for emotion recognition is estimated during the assessments. The results from the music emotion recognition (MER) experiment and three emotional datasets (SEED, DEAP, and MAHNOB) indicate that the classification accuracies achieve 7082% by single-channel data with a 10s time length. Such performances are remarkable when considering minimum data sources as the primary concerns. Furthermore, the individual characteristics in emotion recognition are investigated based on the channels and times found. Therefore, this study provides a novel method to solve single-channel selection for emotion recognition.

Axonal Conduction Delay Shapes the Precision of the Spatial Hearing in A Spiking Neural Network Model of Auditory Brainstem

One method by which the mammalian sound localization pathway localizes sound sources is by analyzing the microsecond-level difference between the arrival times of a sound at the two ears. However, how the neural circuits in the auditory brainstem precisely integrate signals from the two ears, and what the underlying mechanisms are, remains to be understood. Recent studies have reported that variations of axon myelination in the auditory brainstem produces various axonal conduction velocities and sophisticated temporal dynamics, which have not been well characterized in most existing models of sound localization circuits. Here, we present a spiking neural network model of the auditory brainstem to investigate how axon myelinations affect the precision of sound localization. Sound waves with different interaural time differences (ITDs) are encoded and used as stimuli, and the axon properties in the network are adjusted, and the corresponding axonal conduction delays are computed with a multi-compartment axon model. Through the simulation, the sensitivity of ITD perception varies with the myelin thickness of axons in the contralateral input pathways to the medial superior olive (MSO). The ITD perception becomes more precise when the contralateral inhibitory input propagates faster than the contralateral excitatory input. These results indicate that axon myelination and contralateral spike timing influence spatial hearing perception.

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 0.2 s timestamp of the EEG signal has been proposed. Then, to recognize emotional arousal and valence, the similarity measures between pairwise sequences have been performed by dynamic time warping (DTW). After evaluations, the sequence that provides the highest accuracy has been obtained. Thus, the representative channel has been found. Besides, the appropriate time segment for emotion recognition has been estimated. Those findings helpfully exclude redundant data for assessing emotion. Results from the DEAP dataset displayed that the classification accuracies between 72%-75% can be realized by applying the single-channel data with a 5 s length, which is impressive when considering fewer data sources as the primary concern. Hence, the proposed idea would open a new way that uses the similarity measures of sequences for EEG-based emotion recognition.

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. In this work, an integrated planar optrode with a blue LED and microelectrodes was designed and fabricated. The charge transfer mechanism on the interface was theoretically modeled and experimentally verified. An evaluation system for assessing microelectrodes was also built up. Using this system, the proposed model of various biocompatible surface materials on microelectrodes was further investigated under different illumination conditions. The influence of illumination on the microelectrode/electrolyte interface was the cause of optical artifacts, which interfere the biological signal recording. It was found that surface materials had a great effect on the charge transfer capacity, electrical stability and recoverability, photostability, and especially optical artifacts. The metal with better charge transfer capacity and electrical stability is highly possible to have a better performance on the optical artifacts, regardless of its electrical recoverability and photostability under the illumination conditions of optogenetics. Among the five metals used in our investigation, iridium served as the best surface material for the proposed integrated optrodes. Thus, optimizing the surface material for optrodes could reduce optical interference, enhance the quality of the neural signal recording for optogenetics, and thus help to advance the research in neuroscience.

Optical sensor network interrogation system based on nonuniform microwave photonic filters

Based on the nonuniformly spaced microwave photonic delay-line filter technology, a new design of a generic optical fiber sensor network interrogation platform is proposed and demonstrated. Sensing information from different types of optical sensors embedded in filter taps is converted into the variations of delay time and amplitude of each filter tap individually. Information to be measured can be decoded from the complex temporal impulse response of the microwave photonic filter. As proof-of-concept, our proposed approach is verified by simulations and experimental demonstrations successfully. Four optical sensors of different types are simultaneously interrogated via inverse Fourier transform of the filter frequency response. The experiment results show good linearity between the variation of temporal impulse response and the variations of the twist, the lateral pressure, the transversal loading and the temperature. The sensitivity of the sensors in the proposed platform is -2.130×10^-5 a.u/degree, 6.1039 ps/kPa, -1.9146×10^-5 a.u/gram, and 5.1497 ps/°C, respectively. Compared to the conventional optical sensors interrogation system, the presented approach provides a centralized solution that works for different types of optical sensors and can be easily expanded to cover larger optical sensor networks.

A Time-Frequency Measurement and Evaluation Approach for Body Channel Characteristics in Galvanic Coupling Intrabody Communication

Intrabody communication (IBC) can achieve better power efficiency and higher levels of security than other traditional wireless communication technologies. Currently, the majority of research on the body channel characteristics of galvanic coupling IBC are motionless and have only been evaluated in the frequency domain. Given the long measuring times of traditional methods, the access to dynamic variations and the simultaneous evaluation of the time-frequency domain remains a challenge for dynamic body channels such as the cardiac channel. To address this challenge, we proposed a parallel measurement methodology with a multi-tone strategy and a time-parameter processing approach to obtain a time-frequency evaluation for dynamic body channels. A group search algorithm has been performed to optimize the crest factor of multitone excitation in the time domain. To validate the proposed methods, in vivo experiments, with both dynamic and motionless conditions were measured using the traditional method and the proposed method. The results indicate that the proposed method is more time efficient (Tmeas=1 ms) with a consistent performance (ρc > 98%). Most importantly, it is capable of capturing dynamic variations in the body channel and provides a more comprehensive evaluation and richer information for the study of IBC.

Nondestructive and objective assessment of the vestibular function in rodent models: A review

The normal function of the vestibular system is crucial for the sense of balance. The techniques used to assess the vestibular function plays a vital role in the research of the vestibular system. In this article, we have systematically reviewed some popular methods employing vestibular reflexes and vestibular evoked potentials for assessing the vestibular function in rodent models. These vestibular reflexes and vestibular evoked potentials to effective stimuli have been used as nondestructive and objective functional measures. The main types of vestibular reflexes include the vestibulo-ocular reflex (VOR), vestibulocollic reflex (VCR), and vestibulo-sympathetic reflex (VSR). They are all capable of indicating the functions of the semicircular canals and otoliths. However, the VOR assessment is much more prevalently used because of the relatively stereotypical inputoutput relationship and simple motion pattern of the ocular response. In contrast, the complicated motion pattern and small gain of the VCR response, as well as the undesired component possibly contributed from the acceleration receptors outside the labyrinths in the VSR response, restrict the widespread applications of VCR and VSR in the assessment of the vestibular system. The vestibular evoked myogenic potentials (VEMPs) and vestibular sensory evoked potentials (VsEPs) are the two typical evoked potentials that have been also employed for evaluating the vestibular function. Through exploiting different types of the VEMPs, the saccular and utricular functions can be evaluated separately. The sound-induced VEMPs, moreover, are capable of noninvasively assessing the unilateral vestibular function. The VsEPs, via the morphology of their signal waveforms, enable the access to the location-specific information that indicates the functional statuses of different components within the vestibular neural pathway.

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. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.

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. This paper presents a design of LMHF vibrator with multiple plungers to generate vibrational stimulation with ultra low magnetic flux density to minimize the biological effect caused by the EMF. The EMF is analyzed in finite element method (FEM) using COMSOL.

An Assay for Systematically Quantifying the Vestibulo-Ocular Reflex to Assess Vestibular Function in Zebrafish Larvae

Zebrafish (Danio rerio) larvae are widely used to study otic functions because they possess all five typical vertebrate senses including hearing and balance. Powerful genetic tools and the transparent body of the embryo and larva also make zebrafish a unique vertebrate model to study otic development. Due to its small larval size and moisture requirement during experiments, accurately acquiring the vestibulo-ocular reflex (VOR) of zebrafish larva is challenging. In this report, a new VOR testing device has been developed for quantifying linear VOR (LVOR) in zebrafish larva, evoked by the head motion about the earth horizontal axis. The system has a newly designed larva-shaped chamber, by which live fish can be steadily held without anesthesia, and the system is more compact and easier to use than its predecessors. To demonstrate the efficacy of the system, the LVORs in wild-type (WT), dlx3b and dlx4b morphant zebrafish larvae were measured and the results showed that LVOR amplitudes were consistent with the morphological changes of otoliths induced by morpholino oligonucleotides (MO). Our study represents an important advance to obtain VOR and predict the vestibular conditions in zebrafish.

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.

RESULTS

The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design.

SIGNIFICANCE

A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

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.

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. At last, through compare with the experimental results and calculation results, the maximum error of the model is 22.4% and the average error is 14.2% within the frequency range.

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. Image stacks were collected for 3D image rendering and structural segmentation. For anatomical reference, vascular perfusion with fluorescent-conjugated dextran (150 KDa) and trans-corneal perfusion with 0.1 μm fluorescent polystyrene beads were separately performed to identify the episcleral veins (EV) and the trabecular meshwork (TM) and Schlemm's canal (SC), respectively.

RESULTS

Three-dimensional rendering and segmentation of spectral two-photon images revealed detailed structures of the AOS, including SC, collector channels (CC), and aqueous veins (AV). The collagen of the TM was detected proximal to SC. The long and short axes of the SC were 82.2 ± 22.2 μm and 6.7 ± 1.4 μm. The diameters of the CC averaged 25.6 ± 7.9 μm where they originated from the SC (ostia), enlarged to 34.1 ± 13.1 μm at the midpoint, and narrowed to 18.3 ± 4.8 μm at the junction of the AV. The diameter of the AV averaged 12.5 ± 3.4 μm.

CONCLUSIONS

Spectral TPM can be used to reconstruct and measure the spatial relationships of both large and small AOS structures, which will allow for better understanding of distal aqueous outflow dynamics.

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. By using a beam model in undamped conditions and finite-element analysis simulations, the proposed embossed structure showed improvement on the maximum output pressure of the CMUT cell when the embossed pattern was placed on the estimated location of the peak deflection. As compared with a uniform membrane CMUT cell worked in the collapse mode, the proposed CMUT cell can yield the maximum output pressure by 51.1% and 88.1% enhancement with a single embossed pattern made of Si3N4 and nickel, respectively. The maximum output pressures were improved by 34.9% (a single Si3N4 embossed pattern) and 46.7% (a single nickel embossed pattern) with the uniform membrane when the center frequencies of both original and embossed CMUT designs were similar.

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.e. gain variation 1.09-11.70 dB, p < 0.014). The extension, as well as the appearance of joint in IBC channel increases the channel attenuation. While the other gestures and muscle fatigue have negligible effect (gain variation <0.77 dB, p > 0.793) on IBC channel. Moreover, the change of joint angle on human limb IBC channel causes significant variation in bit error rate (BER) performance.

CONCLUSIONS

The results reveal the dynamic behavior of galvanic coupling IBC channel, and provide suggestions for practical IBC system design.

Alpha neurofeedback training improves SSVEP-based BCI performance

OBJECTIVE

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user's SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs.

APPROACH

An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with 'low' performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison.

MAIN RESULTS

The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group.

SIGNIFICANCE

These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects' performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

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). Eleven healthy volunteers completed 25 sessions of NFT in consecutive five days with 5 sessions per day. The results showed that BTR NFT in occipital region did have significant effect on parietal, central and frontal regions, and the changes of BTR and theta amplitude detected in these regions were consistent with the changes at the training location. Moreover, the percentage changes of BTR and theta amplitude in parietal region were significantly greater than those in frontal region probably due to the shorter distance to the training location.

Resting and Initial Beta Amplitudes Predict Learning Ability in Beta/Theta Ratio Neurofeedback Training in Healthy Young Adults

Neurofeedback (NF) training has been proved beneficial in cognitive and behavioral performance improvement in healthy individuals. Unfortunately, the NF learning ability shows large individual difference and in a number of NF studies there are even some non-learners who cannot successfully self-regulate their brain activity by NF. This study aimed to find out the neurophysiological predictor of the learning ability in up-regulating beta-1 (15-18 Hz)/theta (4-7 Hz) ratio (BTR) training in healthy young adults. Eighteen volunteers finished five training sessions in successive 5 days. We found that low beta (12-15 Hz) amplitude in a 1-min eyes-open resting baseline measured before training and the beta-1 amplitude in the first training block with 4.5-min duration could predict the BTR learning ability across sessions. The results provide a low cost, convenient and easy way to predict the learning ability in up-regulating BTR training, and would be helpful in avoiding potential frustration and adjusting training protocol for the participants with poor learning ability.

Dynamic peripheral visual performance relates to alpha activity in soccer players

Many studies have demonstrated the relationship between the alpha activity and the central visual ability, in which the visual ability is usually assessed through static stimuli. Besides static circumstance, however in the real environment there are often dynamic changes and the peripheral visual ability in a dynamic environment (i.e., dynamic peripheral visual ability) is important for all people. So far, no work has reported whether there is a relationship between the dynamic peripheral visual ability and the alpha activity. Thus, the objective of this study was to investigate their relationship. Sixty-two soccer players performed a newly designed peripheral vision task in which the visual stimuli were dynamic, while their EEG signals were recorded from Cz, O1, and O2 locations. The relationship between the dynamic peripheral visual performance and the alpha activity was examined by the percentage-bend correlation test. The results indicated no significant correlation between the dynamic peripheral visual performance and the alpha amplitudes in the eyes-open and eyes-closed resting condition. However, it was not the case for the alpha activity during the peripheral vision task: the dynamic peripheral visual performance showed significant positive inter-individual correlations with the amplitudes in the alpha band (8–12 Hz) and the individual alpha band (IAB) during the peripheral vision task. A potential application of this finding is to improve the dynamic peripheral visual performance by up-regulating alpha activity using neuromodulation techniques.

An adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection

Ultrasonic diagnosis that is convenient and nondestructive to the human body is widely used in medicine. In clinical, ultrasonic backscattered signals characteristics are utilized to acquire information of the human body tissues to perform diagnosis. In this paper, an adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection based on the marginal spectrum is presented. In the beginning, the ultrasonic backscattered signal is decomposed into a series of intrinsic mode functions (IMFs) by the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Then the Hilbert spectrum is gained by the Hilbert transform on the IMFs decomposed and screened. Finally, the time-frequency information in the Hilbert spectrum is utilized to extract the instantaneous characteristic frequency based on the marginal spectrum features to detect the objective. With this technique, the spacing between tissues can be estimated for tissue characterization by processing multiple echoes even in the complicated environment. In the simulation study, comparing with the FFT, the technique presented shows its strong noise immunity and indicates its validity in instantaneous characteristic frequency detection.

Resting alpha activity predicts learning ability in alpha neurofeedback

Individuals differ in their ability to learn how to regulate the brain activity by neurofeedback. This study aimed to investigate whether the resting alpha activity can predict the learning ability in alpha neurofeedback. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback and the learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback.

Bit error rate estimation for galvanic-type intra-body communication using experimental eye-diagram and jitter characteristics

Bit error rate (BER), which indicates the reliability of communicate channel, is one of the most important values in all kinds of communication system, including intra-body communication (IBC). In order to know more about IBC channel, this paper presents a new method of BER estimation for galvanic-type IBC using experimental eye-diagram and jitter characteristics. To lay the foundation for our methodology, the fundamental relationships between eye-diagram, jitter and BER are first reviewed. Then experiments based on human lower arm IBC are carried out using quadrature phase shift keying (QPSK) modulation scheme and 500 KHz carries frequency. In our IBC experiments, the symbol rate is from 10 Ksps to 100 Ksps, with two transmitted power settings, 0 dBm and -5 dBm. Finally, the BER results were obtained after calculation by experimental data through the relationships among eye-diagram, jitter and BER. These results are then compared with theoretical values and they show good agreement, especially when SNR is between 6 dB to 11 dB. Additionally, these results demonstrate assuming the noise of galvanic-type IBC channel as Additive White Gaussian Noise (AWGN) in previous study is applicable.

Flashing color on the performance of SSVEP-based brain-computer interfaces

A critical problem in using steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) for clinical and commercial use is the visual fatigue the user may suffer when staring at flashing stimuli. Aiming at the design of user-friendly BCIs with satisfactory performance, this work is to preliminarily investigate how different colors influence the SSVEP (i.e. frequency or phase) and system performance. The results show that white stimuli can lead to the highest performance, followed by gray, red, green and blue stimuli.

Signal transmission through human muscle for implantable medical devices using galvanic intra-body communication technique

Signal transmission over human tissues has long been the center research topic for biomedical engineering in both academic and industrial arenas. This is particular important for implantable medical devices (IMD) to communicate with other sensor devices in achieving health care and monitoring functions. Traditional Radio Frequency (RF) transmission technique suffers from not only high attenuation but also potential interference & eavesdropping. This paper has examined the alternate galvanic type Intra-Body Communication Technique (IBC) in transmitting signal across the body tissue (mainly muscle) in both analytical electromagnetic model with simulation results. Comparisons of these results with traditional RF data in literatures show a high promising potential (saving over 10 dB or more in path loss) for IBC transmission. Concrete discussions and several further research directions are also given out at the end of this paper.

Individual alpha neurofeedback training effect on short term memory

Memory performance has been reported to be associated with electroencephalogram (EEG) alpha activity. This study aimed to improve short term memory performance by individual alpha neurofeedback training (NFT). With appropriate protocol designed for NFT, the experimental results showed that the participants were able to learn to increase the relative amplitude in individual alpha band during NFT and short term memory performance was significantly enhanced by 20 sessions of NFT. More importantly, further analysis revealed that the improvement of short term memory was positively correlated with the increase of the relative amplitude in the individual upper alpha band during training. In addition, effective strategies for individual alpha training varied among individuals and the most successful mental strategies were related to positive thinking.

Quasi-static modeling of human limb for intra-body communications with experiments

In recent years, the increasing number of wearable devices on human has been witnessed as a trend. These devices can serve for many purposes: personal entertainment, communication, emergency mission, health care supervision, delivery, etc. Sharing information among the devices scattered across the human body requires a body area network (BAN) and body sensor network (BSN). However, implementation of the BAN/BSN with the conventional wireless technologies cannot give optimal result. It is mainly because the high requirements of light weight, miniature, energy efficiency, security, and less electromagnetic interference greatly limit the resources available for the communication modules. The newly developed intra-body communication (IBC) can alleviate most of the mentioned problems. This technique, which employs the human body as a communication channel, could be an innovative networking method for sensors and devices on the human body. In order to encourage the research and development of the IBC, the authors are favorable to lay a better and more formal theoretical foundation on IBC. They propose a multilayer mathematical model using volume conductor theory for galvanic coupling IBC on a human limb with consideration on the inhomogeneous properties of human tissue. By introducing and checking with quasi-static approximation criteria, Maxwell's equations are decoupled and capacitance effect is included to the governing equation for further improvement. Finally, the accuracy and potential of the model are examined from both in vitro and in vivo experimental results.

Trial pruning for classification of single-trial EEG data during motor imagery

Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After trial pruning, a subset of the EEG data are selected, on which common spatial pattern (CSP) and Gaussian classifier are trained. The performance of the proposed method is tested on Data set IIa of BCI Competition IV, and the simulation result demonstrates a significant improvement for six out of nine subjects.

Multilayer limb quasi-static electromagnetic modeling with experiments for Galvanic coupling type IBC

Intra-body communication (IBC) is a new, emerging, short-range and human body based communication methodology. It is a technique to network various devices on human body, by utilizing the conducting properties of human tissues. For currently fast developed Body area network(BAN)/Body sensor network(BSN), IBC is believed to have advantages in power consumption, electromagnetic radiation, interference from external electromagnetic noise, security, and restriction in spectrum resource. In this article, the authors propose an improved mathematical model, which includes both electrical properties and proportion of human tissues, for IBC on a human limb. By solving the mathematical model analytically on four-layer system (skin, fat, muscle, and bone) and conducting in-vivo experiment, a comparison has been conducted.

Simple electrical model and initial experiments for intra-body communications

Intra-Body Communication(IBC) is a short range "wireless" communication technique appeared in recent years. This technique relies on the conductive property of human tissue to transmit the electric signal among human body. This is beneficial for devices networking and sensors among human body, and especially suitable for wearable sensors, telemedicine system and home health care system as in general the data rates of physiologic parameters are low. In this article, galvanic coupling type IBC application on human limb was investigated in both its mathematical model and related experiments. The experimental results showed that the proposed mathematical model was capable in describing the galvanic coupling type IBC under low frequency. Additionally, the calculated result and experimental result also indicated that the electric signal induced by the transmitters of IBC can penetrate deep into human muscle and thus, provide an evident that IBC is capable of acting as networking technique for implantable devices.

Modeling for intra-body communication with bone effect

Intra-body communication (IBC) is a new, different "wireless" communication technique based on the human tissue. This short range "wireless" communication technology provides an alternative solution to wearable sensors, home health system, telemedicine and implanted devices. The development of the IBC enables the possibilities of providing less complexity and convenient communication methodologies for these devices. By regarding human tissue as communication channel, IBC making use of the conductivities properties of human tissue to send electrical signal from transmitter to receiver. In this paper, the authors proposed a new mathematical model for galvanic coupling type IBC based on a human limb. Starting from the electromagnetic theory, the authors treat human tissue as volume conductor, which is in analogous with the bioelectric phenomena analysis. In order to explain the mechanism of galvanic coupling type technique of IBC, applying the quasi-static approximation, the governing equation can be reduced to Laplace Equation. Finally, the analytical model is evaluated with on-body measurement for testing its performance. The comparison result shows that the developed mathematical model can provide good approximation for galvanic coupling type IBC on human limb under low operating frequencies.

ECG QRS Complex detection with programmable hardware

In recent years, algorithm based on Mathematical Morphology and wavelet transform has been proposed for ECG QRS Complex detection. However, its intensity of computation is high. This paper proposes the algorithm and hardware architecture for the whole system of QRS Complex detection based on Mathematical Morphology and Quadratic Spline wavelet transform, with implementation in Field Programmable Gate Array (FPGA). The system consists of Morphological filtering, Quadratic Spline wavelet transform and Modulus Maxima Pair Recognition modules. The parallel and pipelined architecture of system can operate in the maximum 35MHz with throughput of one sample per clock cycle. The QRS Complex detection accuracy for MIT/BIH arrhythmia database recordings and resource consumption are reported. The design is suitable for both batch processing of huge volume ECG data and real time applications for portable devices.

Flexible implementation of front-end bioelectric signal amplifier using FPAA for telemedicine system

Traditional/Current electronic circuits for Telemedicine have significant performance on certain bioelectric signal detection. However, it is rarely seen that can handle multiple signals without changing of hardware. This paper introduces a general front-end amplifier for various bioelectric signals based on Field Programmable Analogy Array (FPAA) Technology. Employing FPAA technology, the implemented amplifier can be adapted for various bioelectric signals without alternating the circuitry while its compact size (core parts < 2 cm2) provides an alternative solution for miniaturized Telemedicine system and Wearable Devices. The proposed design implementation has demonstrated, through successfully ECG and EMG signal extractions, a quick way to miniaturize analog biomedical circuit in a convenient and cost effective way.

Analysis of op-amp power-supply current sensing current-mode instrumentation amplifier for biosignal acquisition system

Common biosignals are frequently recorded and used in modern clinical practice and even daily health care applications. The most important building block of such an acquisition system is the analog readout front end. Usually, an instrumentation amplifier with high CMRR and configurable gain characteristics is suitable for this application. There is a current mode instrumentation amplifier based on op amp power supply current sensing technique, which is a possible solution for designing the biosignal acquisition system. This paper makes a brief analysis of this topology and studies some features for applying to biosignals. Furthermore, a real circuit simulation analysis employing CMOS 0.35 microm technology under a 3 V power supply is conducted.

A front-end platform of the network-based intelligent home healthcare embedded system

The aim of this article is to independently implement an indispensable front-end platform in a network-based intelligent home healthcare system. We propose to realize an ARM-cored structure embedded with muClinux system to integrate several kinds of medical measuring modules with our platform. Then we demonstrate the platform work cooperatively with all proposed and built -units. This most characteristic point of this platform lies in the implementation of the embedded expert system on the hardware basis. This kind of feature allows those aged and/or long-time patients to get medical diagnosis and advices at home, and, at the same time makes doctors work more effectively and pertinently.