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.

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. In the dynamic range of 20-40 °C, we obtained a good temperature sensitivity of -1.29 nm/°C, with linear fitting up to 0.998. Benefiting from the advantages of laser sensing, the acquired laser has a 3 - dB bandwidth of less than 0.2 nm and a signal-to-noise ratio (SNR) of up to 40 dB. The proposed sensor has a low cost and high sensitivity, which is expected to be used in biomedical health detection, real-time monitoring of ocean temperature, and other application scenarios.

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. In this work, with the help of similarity measure methods, the asymmetric features are extracted from the sequences generated by different channel data. After evaluating all asymmetric features for emotion recognition, the optimal feature that yields remarkable accuracy is identified. Therefore, the classification task can be accomplished through a small amount of channel data. From a music emotion recognition experiment and a public DEAP dataset, the classification accuracies of various test sets are approximately 80-85% when employing an optimal feature extracted from one pair of symmetrical channels. Such performances are impressive when using fewer resources is a concern. Further investigation revealed that emotion recognition shows strongly individual characteristics, so an appropriate solution is to include the subject-dependent properties. Compared to the existing works, this method benefits from the design of a portable emotion-aware device used during self-isolation, as fewer scalp sensors are needed. Hence, it would provide a novel way to realize emotional applications in the future.

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 0.2 s 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 70-82% by single-channel data with a 10 s 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.

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.

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. An annular nickel pattern with the dimension of 3 µm × 2 µm (width × height) was formed on a full top electrode CMUT to realize an embossed CMUT cell. Experimental characterization was carried out with optical, electrical, and acoustic instruments on the embossed and uniform CMUT cells. The embossed CMUT cell achieved 27.1% improvement of output pressure in comparison to the uniform CMUT cell biased at 170 V voltage. The fractional bandwidths of the embossed and uniform CMUT cells were 52.5% and 41.8%, respectively. It substantiated that the embossed pattern should be placed at the vibrating center of the membrane for achieving a higher output pressure. The experimental characterization indicated that the embossed CMUT cell has better operational performance than the uniform CMUT cell in collapse region.

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. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. MAIN RESULTS: Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. SIGNIFICANCE: A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.

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.

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. In this paper, a monolithic multiband capacitive micromachined ultrasonic transducer (CMUT) array was designed and fabricated for the reception of the wideband PA signals so as to provide high-resolution images with high-frequency CMUT arrays and present the high signal-to-noise-ratio major structure with low-frequency CMUT arrays. To demonstrate its performance, a phantom experiment was conducted to show and evaluate the various qualities of multiresolution images. In addition, an in vivo mouse model experiment was also carried out for revealing the multiscale PA imaging capability with the multiband CMUTs on biological tissues. From the obtained results, the images from different CMUT arrays could show the structures of the mouse brain in different scales. In addition, the images from the high-frequency CMUT arrays were able to reveal the major blood vasculatures, whereas the images from low-frequency CMUT arrays showed the gross macroscopic anatomy of the brain with higher contrast.

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. The MBPAT imaging system was examined by the phantom experiment, and then was successfully applied to image the zebrafish in vivo. The imaging results indicated that CMUT-array-based MBPAT can provide a more comprehensive and accurate characterization of biological tissues, which exhibit the potential of MBPAT/CMUT in various areas of biomedical imaging.

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.

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. In this context, this work develops a calibration strip for ICG assay photoelectric detection systems. An image of the test strip is captured by an image acquisition device, followed by performing a fuzzy c-means (FCM) clustering algorithm and maximin-distance algorithm for image segmentation. Additionally, experiments are conducted to find the best characteristic quantity. By analyzing the linear coefficient, an average value of hue (H) at 14 min is chosen as the characteristic quantity and the empirical formula between H and optical density (OD) value is established. Therefore, H, saturation (S), and value (V) are calculated by a number of selected OD values. Then, H, S, and V values are transferred to the RGB color space and a high-resolution printer is used to print the strip images on cellulose nitrate membranes. Finally, verification of the printed calibration strips is conducted by analyzing the linear correlation between OD and the spectral reflectance, which shows a good linear correlation (R² = 98.78%).

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.

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.

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.

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.

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. This paper proposes a field-circuit finite element method (FEM) model of galvanic coupling IBC in a real measurement environment to estimate the human channel gain. First an anisotropic concentric cylinder model of the electric field intra-body communication for human limbs was developed based on the galvanic method. Then the electric field model was combined with several impedance elements, which were equivalent in terms of parasitic impedance of the electrodes, input and output impedance of the transceiver, establishing a field-circuit FEM model. The results indicated that a circuit module equivalent to external factors can be added to the field-circuit model, which makes this model more complete, and the estimations based on the proposed field-circuit are in better agreement with the corresponding measurement results.

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.

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.