[Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.

Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy.

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.

Adaptive logical stochastic resonance in time-delayed synthetic genetic networks.

In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.

A Novel Two-Wire Fast Readout Approach for Suppressing Cable Crosstalk in a Tactile Resistive Sensor Array.

For suppressing the crosstalk problem due to wire resistances and contacted resistances of the long flexible cables in tactile sensing systems, we present a novel two-wire fast readout approach for the two-dimensional resistive sensor array in shared row-column fashion. In the approach, two wires are used for every driving electrode and every sampling electrode in the resistive sensor array. The approach with a high readout rate, though it requires a large number of wires and many sampling channels, solves the cable crosstalk problem. We also verified the approach's performance with Multisim simulations and actual experiments.

Cable Crosstalk Suppression with Two-Wire Voltage Feedback Method for Resistive Sensor Array.

Using a long, flexible test cable connected with a one-wire voltage feedback circuit, a resistive tactile sensor in a shared row-column fashion exhibited flexibility in robotic operations but suffered from crosstalk caused by the connected cable due to its wire resistances and its contacted resistances. Firstly, we designed a new non-scanned driving-electrode (VF-NSDE) circuit using two wires for every row line and every column line to reduce the crosstalk caused by the connected cables in the circuit. Then, an equivalent resistance expression of the element being tested (EBT) for the two-wire VF-NSDE circuit was analytically derived. Following this, the one-wire VF-NSDE circuit and the two-wire VF-NSDE circuit were evaluated by simulation experiments. Finally, positive features of the proposed method were verified with the experiments of a two-wire VF-NSDE prototype circuit. The experiment results show that the two-wire VF-NSDE circuit can greatly reduce the crosstalk error caused by the cables in the 2-D networked resistive sensor array.

An Improved Zero Potential Circuit for Readout of a Two-Dimensional Resistive Sensor Array.

With one operational amplifier (op-amp) in negative feedback, the traditional zero potential circuit could access one element in the two-dimensional (2-D) resistive sensor array with the shared row-column fashion but it suffered from the crosstalk problem for the non-scanned elements' bypass currents, which were injected into array's non-scanned electrodes from zero potential. Firstly, for suppressing the crosstalk problem, we designed a novel improved zero potential circuit with one more op-amp in negative feedback to sample the total bypass current and calculate the precision resistance of the element being tested (EBT) with it. The improved setting non-scanned-electrode zero potential circuit (S-NSE-ZPC) was given as an example for analyzing and verifying the performance of the improved zero potential circuit. Secondly, in the S-NSE-ZPC and the improved S-NSE-ZPC, the effects of different parameters of the resistive sensor arrays and their readout circuits on the EBT's measurement accuracy were simulated with the NI Multisim 12. Thirdly, part features of the improved circuit were verified with the experiments of a prototype circuit. Followed, the results were discussed and the conclusions were given. The experiment results show that the improved circuit, though it requires one more op-amp, one more resistor and one more sampling channel, can access the EBT in the 2-D resistive sensor array more accurately.

Design and Calibration of a New 6 DOF Haptic Device.

For many applications such as tele-operational robots and interactions with virtual environments, it is better to have performance with force feedback than without. Haptic devices are force reflecting interfaces. They can also track human hand positions simultaneously. A new 6 DOF (degree-of-freedom) haptic device was designed and calibrated in this study. It mainly contains a double parallel linkage, a rhombus linkage, a rotating mechanical structure and a grasping interface. Benefited from the unique design, it is a hybrid structure device with a large workspace and high output capability. Therefore, it is capable of multi-finger interactions. Moreover, with an adjustable base, operators can change different postures without interrupting haptic tasks. To investigate the performance regarding position tracking accuracy and static output forces, we conducted experiments on a three-dimensional electric sliding platform and a digital force gauge, respectively. Displacement errors and force errors are calculated and analyzed. To identify the capability and potential of the device, four application examples were programmed.

Removal of EOG artifacts from EEG recordings using stationary subspace analysis.

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

Synchronization control for stochastic neural networks with mixed time-varying delays.

Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results.

A novel crosstalk suppression method of the 2-D networked resistive sensor array.

The 2-D resistive sensor array in the row-column fashion suffered from the crosstalk problem for parasitic parallel paths. Firstly, we proposed an Improved Isolated Drive Feedback Circuit with Compensation (IIDFCC) based on the voltage feedback method to suppress the crosstalk. In this method, a compensated resistor was specially used to reduce the crosstalk caused by the column multiplexer resistors and the adjacent row elements. Then, a mathematical equivalent resistance expression of the element being tested (EBT) of this circuit was analytically derived and verified by the circuit simulations. The simulation results show that the measurement method can greatly reduce the influence on the EBT caused by parasitic parallel paths for the multiplexers' channel resistor and the adjacent elements.

A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch.

Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures.

[Short-term memory characteristics of vibration intensity tactile perception on human wrist].

In this study, a recall experiment and a recognition experiment were designed to assess the human wrist's short-term memory characteristics of tactile perception on vibration intensity, by using a novel homemade vibrotactile display device based on the spatiotemporal combination vibration of multiple micro vibration motors as a test device. Based on the obtained experimental data, the short-term memory span, recognition accuracy and reaction time of vibration intensity were analyzed. From the experimental results, some important conclusions can be made: (1) The average short-term memory span of tactile perception on vibration intensity is 3 ± 1 items; (2) The greater difference between two adjacent discrete intensities of vibrotactile stimulation is defined, the better average short-term memory span human wrist gets; (3) There is an obvious difference of the average short-term memory span on vibration intensity between the male and female; (4) The mechanism of information extraction in short-term memory of vibrotactile display is to traverse the scanning process by comparison; (5) The recognition accuracy and reaction time performance of vibrotactile display compares unfavourably with that of visual and auditory. The results from this study are important for designing vibrotactile display coding scheme.

Construction of chiral bridged tricyclic benzopyrans: enantioselective catalytic Diels-Alder reaction and a one-pot reduction/acid-catalyzed stereoselective cyclization.

An asymmetric two-step approach to chiral bridged tricyclic benzopyrans, core structures featured in various natural products, is described. In the synthesis, an unprecedented enantioselective catalytic decarboxylative Diels-Alder reaction is developed using readily available coumarin-3-carboxylic acids and aldehydes as reactants under mild reaction conditions. Notably, the decarboxylation-assisted release of the catalyst enables the process to proceed efficiently with high enantio- and diastereoselectivity. Furthermore, a one-pot procedure for either a LiAlH4 - or NaBH4 -mediated reduction with subsequent acid-catalyzed intramolecular cyclization of the Diels-Alder adducts was identified for the efficient formation of the chiral bridged tricyclic benzopyrans.

Bionic Snail Robot Enhanced by Poroelastic Foams Crawls Using Direct and Retrograde Waves.

Snails employ a distinctive crawling mechanism in which the pedal waves travel along the foot and interact with the mucus to promote efficient movement on various substrates. Inspired by the concavities on the pedal wave, we develop a new bionic snail robot that introduces transverse patterns in a longitudinal wave to periodically change the friction. The poroelastic foam serves as flexible constraint and fills the robot's internal cavity. It contributes to the bending action, and maintains the thinness and softness of the robot. Then, the model of the robot's single segment is built utilizing the Euler-Bernoulli beam theory. The model aligns well with the experimental data, thereby confirming the effectiveness of soft constraints. The evaluation of pedal wave is conducted, which further guides the optimization of the control sequence. The experiments demonstrated the robot performing retrograde wave locomotion on dry substrates. Notably, shear-thickening fluids were found to be suitable for this particular crawling pattern compared with other mucus simulants, resulting in direct wave locomotion with a 49% increase in speed and a 33% reduction in energy usage. The load capacity of the soft snail robot was also enhanced, enabling it to carry loads up to 2.84 times its own weight. The use of mucus in crawling also brings valuable insights for the enhancement of other biomimetic robots.

SleepFC: Feature Pyramid and Cross-Scale Context Learning for Sleep Staging.

Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue: 1) how to effectively learn the intrinsic features of salient waves from single-channel EEG signals; 2) how to learn and capture the useful information of sleep stage transition rules; 3) how to address the class imbalance problem of sleep stages. To handle these problems in sleep staging, we propose a novel method named SleepFC. This method comprises convolutional feature pyramid network (CFPN), cross-scale temporal context learning (CSTCL), and class adaptive fine-tuning loss function (CAFTLF) based classification network. CFPN learns the multi-scale features from salient waves of EEG signals. CSTCL extracts the informative multi-scale transition rules between sleep stages. CAFTLF-based classification network handles the class imbalance problem. Extensive experiments on three public benchmark datasets demonstrate the superiority of SleepFC over the state-of-the-art approaches. Particularly, SleepFC has a significant performance advantage in recognizing the N1 sleep stage, which is challenging to distinguish.

Progress and challenges in bacterial infection theranostics based on functional metal nanoparticles.

The rapid proliferation and infection of bacteria, especially multidrug-resistant bacteria, have become a great threat to global public health. Focusing on the emergence of "super drug-resistant bacteria" caused by the abuse of antibiotics and the insufficient and delayed early diagnosis of bacterial diseases, it is of great research significance to develop new technologies and methods for early targeted detection and treatment of bacterial infection. The exceptional effects of metal nanoparticles based on their unique physical and chemical properties make such systems ideal for the detection and treatment of bacterial infection both in vitro and in vivo. Metal nanoparticles also have admirable clinical application prospects due to their broad antibacterial spectrum, various antibacterial mechanisms and excellent biocompatibility. Herein, we summarized the research progress concerning the mechanism of metal nanoparticles in terms of antibacterial activity together with the detection of bacterial. Representative achievements are selected to illustrate the proof-of-concept in vitro and in vivo applications. Based on these observations, we also give a brief discussion on the current problems and perspective outlook of metal nanoparticles in the diagnosis and treatment of bacterial infection.

A bronchoscopic navigation method based on neural radiation fields.

PURPOSE: We introduce a novel approach for bronchoscopic navigation that leverages neural radiance fields (NeRF) to passively locate the endoscope solely from bronchoscopic images. This approach aims to overcome the limitations and challenges of current bronchoscopic navigation tools that rely on external infrastructures or require active adjustment of the bronchoscope. METHODS: To address the challenges, we leverage NeRF for bronchoscopic navigation, enabling passive endoscope localization from bronchoscopic images. We develop a two-stage pipeline: offline training using preoperative data and online passive pose estimation during surgery. To enhance performance, we employ Anderson acceleration and incorporate semantic appearance transfer to deal with the sim-to-real gap between training and inference stages. RESULTS: We assessed the viability of our approach by conducting tests on virtual bronchscopic images and a physical phantom against the SLAM-based methods. The average rotation error in our virtual dataset is about 3.18 ∘ and the translation error is around 4.95 mm. On the physical phantom test, the average rotation and translation error are approximately 5.14 ∘ and 13.12 mm. CONCLUSION: Our NeRF-based bronchoscopic navigation method eliminates reliance on external infrastructures and active adjustments, offering promising advancements in bronchoscopic navigation. Experimental validation on simulation and real-world phantom models demonstrates its efficacy in addressing challenges like low texture and challenging lighting conditions.

Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy activity samples to be predicted at shallower layers, without executing deeper layers, hence leading to notable adaptiveness in terms of accuracy-speed trade-off under varying resource demands. However, prior most works typically optimize all the classifiers equally on all types of activity data. As a result, deeper classifiers will only see hard samples during test phase, which renders the model suboptimal due to the training-test data distribution mismatch. Such issue has been rarely explored in the context of activity recognition. In this paper, to close the gap, we propose to organize all these classifiers as a dynamic-depth network and jointly optimize them in a similar gradient-boosting manner. Specifically, a gradient-rescaling is employed to bound the gradients of parameters at different depths, that makes such training procedure more stable. Particularly, we perform a prediction reweighting to emphasize current deep classifier while weakening the ensemble of its previous classifiers, so as to relieve the shortage of training data at deeper classifiers. Comprehensive experiments on multiple HAR benchmarks including UCI-HAR, PAMAP2, UniMiB-SHAR, and USC-HAD verify that it is state-of-the-art in accuracy and speed. A real implementation is measured on an ARM-based mobile device.

A Wearable Fingertip Force Feedback Device System for Object Stiffness Sensing.

Virtual reality technology brings a new experience to human-computer interaction, while wearable force feedback devices can enhance the immersion of users in interaction. This paper proposes a wearable fingertip force feedback device that uses a tendon drive mechanism, with the aim of simulating the stiffness characteristics of objects within virtual scenes. The device adjusts the rotation angle of the torsion spring through a DC motor, and then uses a wire to convert the torque into a feedback force at the user's index fingertips, with an output force of up to 4 N and a force change rate of up to 10 N/s. This paper introduces the mechanical structure and design process of the force feedback device, and conducts a mechanical analysis of the device to select the appropriate components. Physical and psychological experiments are conducted to comprehensively evaluate the device's performance in conveying object stiffness information. The results show that the device can simulate different stiffness characteristics of objects, and users can distinguish objects with different stiffness characteristics well when wearing the force feedback device and interacting with the three-dimensional virtual environments.

MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition.

Self-supervised Human Activity Recognition (HAR) has been gradually gaining a lot of attention in ubiquitous computing community. Its current focus primarily lies in how to overcome the challenge of manually labeling complicated and intricate sensor data from wearable devices, which is often hard to interpret. However, current self-supervised algorithms encounter three main challenges: performance variability caused by data augmentations in contrastive learning paradigm, limitations imposed by traditional self-supervised models, and the computational load deployed on wearable devices by current mainstream transformer encoders. To comprehensively tackle these challenges, this paper proposes a powerful self-supervised approach for HAR from a novel perspective of denoising autoencoder, the first of its kind to explore how to reconstruct masked sensor data built on a commonly employed, well-designed, and computationally efficient fully convolutional network. Extensive experiments demonstrate that our proposed Masked Convolutional AutoEncoder (MaskCAE) outperforms current state-of-the-art algorithms in self-supervised, fully supervised, and semi-supervised situations without relying on any data augmentations, which fills the gap of masked sensor data modeling in HAR area. Visualization analyses show that our MaskCAE could effectively capture temporal semantics in time series sensor data, indicating its great potential in modeling abstracted sensor data. An actual implementation is evaluated on an embedded platform.