Signal Acquisition Method Research Articles

Which ERP components are effective in measuring cognitive load in multimedia learning? A meta-analysis based on relevant studies.

The open and generative nature of multimedia learning environments tends to cause cognitive overload in learners, and cognitive load is difficult for researchers to observe objectively because of its implicit and complex nature. Event-related potentials (ERP), a method of studying potential changes associated with specific events or stimuli by recording the electroencephalogram (EEG), has become an important method of measuring cognitive load in cognitive psychology. Although many studies have relied on ERP output measurements to compare different levels of cognitive load in multimedia learning, the results of the effect of cognitive load on ERP have been inconsistent. In this study, we used a meta-analysis of evidence-based research to quantitatively analyze 17 experimental studies to quantitatively evaluate which ERP component (amplitude) is most sensitive to cognitive load. Forty five effect sizes from 26 studies involving 360 participants were calculated. (1) The results of the studies analyzed in subgroups indicated high level effect sizes for P300 and P200 (2) Analyses of moderating variables for signal acquisition did not find that different methods of signal acquisition had a significant effect on the measurement of cognitive load (3) Analyses of moderating variables for task design found that a task system with feedback was more convenient for the measurement of cognitive load, and that designing for 3 levels of cognitive load was more convenient for the measurement of cognitive load than for 2 levels of cognitive load. (4) Analyses of continuous moderating variables for subject characteristics did not find significant effects of age, gender, or sample size on the results.

Restriction-induced time-dependent transcytolemmal water exchange: Revisiting the Kӓrger exchange model

The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 μm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Integrated Photoelectrochemical-SERS Platform Based on Plasmonic Metal-Semiconductor Heterostructures for Multidimensional Charge Transfer Analysis and Enhanced Patulin Detection.

Comprehending the charge transfer mechanism at the semiconductor interfaces is crucial for enhancing the electronic and optical performance of sensing devices. Yet, relying solely on single signal acquisition methods at the interface hinders a comprehensive understanding of the charge transfer under optical excitation. Herein, we present an integrated photoelectrochemical surface-enhanced Raman spectroscopy (PEC-SERS) platform based on quantum dots/metal-organic framework (CdTe/Yb-TCPP) nanocomposites for investigating the charge transfer mechanism under photoexcitation in multiple dimensions. This integrated platform allows simultaneous PEC and SERS measurements with a 532 nm laser. The obtained photocurrent and Raman spectra of the CdTe/Yb-TCPP nanocomposites are simultaneously influenced by variable bias voltages, and the correlation between them enables us to predict the charge transfer pathway. Moreover, we integrate gold nanorods (Au NRs) into the PEC-SERS system by using magnetic separation and DNA biometrics to construct a biosensor for patulin detection. This biosensor demonstrates the voltage-driven ON/OFF switching of PEC and SERS signals, a phenomenon attributed to the plasmon resonance effect of Au NRs at different voltages, thereby influencing charge transfer. The detection of patulin in apples verified the applicability of the biosensor. The study offers an efficient approach to understanding semiconductor-metal interfaces and presents a new avenue for designing high-performance biosensors.

Multi-frequency signal acquisition and phase measurement in space gravitational wave detection.

To enhance the accuracy of phase measurement and to prevent tracking errors, it is crucial to effectively read the multi-frequency signal in space gravitational wave detection. In this paper, a novel signal acquisition method called the multi-frequency acquisition algorithm is proposed and implemented. Different from the traditional single-frequency acquisition, the signal characteristics of amplitude and frequency are both considered to better distinguish different frequency components. A phasemeter integrated with the acquisition method and narrow-bandwidth digital phase-locked loop is constructed for the method test and verification. The results show that the multi-frequency acquisition unit can capture all the frequencies of an input signal in several milliseconds. The precision is better than ±200Hz under a low SNR (signal-to-noise ratio) of 0 dB. The phase noise can reach 2 µrad/Hz1/2 in the frequency range of 0.1-1Hz and satisfy the requirement of the space gravitational wave detection in all frequency ranges.

Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning.

The elevator door system plays a crucial role in ensuring elevator safety. Fault prediction is an invaluable tool for accident prevention. By analyzing the sound signals generated during operation, such as component wear and tear, the fault of the system can be accurately determined. This study proposes a GNN-LSTM-BDANN deep learning model to account for variations in elevator operating environments and sound signal acquisition methods. The proposed model utilizes the historical sound data from other elevators to predict the remaining useful life (RUL) of the target elevator door system. Firstly, the opening and closing sounds of other elevators is collected, followed by the extraction of relevant sound signal characteristics including A-weighted sound pressure level, loudness, sharpness, and roughness. These features are then transformed into graph data with geometric structure representation. Subsequently, the Graph Neural Networks (GNN) and long short-term memory networks (LSTM) are employed to extract deeper features from the data. Finally, transfer learning based on the improved Bhattacharyya Distance domain adversarial neural network (BDANN) is utilized to transfer knowledge learned from historical sound data of other elevators to predict RUL for the target elevator door system effectively. Experimental results demonstrate that the proposed method can successfully predict potential failure timeframes for different elevator door systems.

Water-resistant conformal hydrogels toward underwater human-machine interfaces based on synergistic immersion method and supramolecular interactions strategy

Flexible electronic skin based on hydrogel provides a non-invasive method for real-time acquisition of body movement signals and biosignals, which is essential for future smart health monitoring and healthcare. However, it is critical yet challenging to achieve motional and physiological signals monitoring in an aquatic environment because the interface electrodes may lose their conformability under wet environments. Here, inspired by the CB[8] enhanced hydrophobic polar–π interactions, hydrogel electrode based on the synergistic immersion method and supramolecular interactions is introduced, which exhibits excellent underwater conformability while maintaining tissue-mimetic mechanical properties. No need to add any organic solvents or hydrophobic monomers, the immersion behavior strengthens the non-covalent interactions in the network, and the conformal hydrogel sensor based on hydrophilic monomers has outstanding underwater adhesion capacity (adhesion strength of 139.62 kPa), excellent mechanical behavior (toughness of 5812.31 kJ m−3), and superior underwater stability (swelling ratio of 0.01) by rationally modulating the gel factors and immersion time. The ability to sensitively monitor movement signals and electrocardiogram (ECG) in multiple wet environments offers unique prospects for the development of next-generation wearable medical devices based on the facile preparation strategy.

COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

High-density analysis of surface wave profile imaging based on a multiple coverage common-midpoint signal-couple array

Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.

EEG Signal Analysis for Monitoring Concentration of Operators

Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavourable conditions, e.g. high temperatures continuously for a long period of time. This has a huge impact on their concentration, which usually determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows determining, for example, whether the operator is able to focus on the realization of his tasks. The main goal of the article was to develop an algorithm for determining the state of brain activity by analysing the EEG signal. For this purpose, methods of EEG signal acquisition were described, basic types of brain waves were discussed, and exemplary states of brain activity were recorded. Particular attention was paid to technical aspects related to signal analysis. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyser were also presented.

EEG Signal Analysis for Monitoring Concentration of Operators

Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavourable conditions, e.g. high temperatures continuously for a long period of time. This has a huge impact on their concentration, which usually determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows determining, for example, whether the operator is able to focus on the realization of his tasks. The main goal of the article was to develop an algorithm for determining the state of brain activity by analysing the EEG signal. For this purpose, methods of EEG signal acquisition were described, basic types of brain waves were discussed, and exemplary states of brain activity were recorded. Particular attention was paid to technical aspects related to signal analysis. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyser were also presented.

EEG Signal Analysis for Monitoring Concentration of Operators

Often, operators of machines, including unmanned ground vehicles (UGVs) or working machines, are forced to work in unfavourable conditions, e.g. high temperatures continuously for a long period of time. This has a huge impact on their concentration, which usually determines the success of many tasks entrusted to them. Electroencephalography (EEG) allows the study of the electrical activity of the brain. It allows determining, for example, whether the operator is able to focus on the realization of his tasks. The main goal of the article was to develop an algorithm for determining the state of brain activity by analysing the EEG signal. For this purpose, methods of EEG signal acquisition were described, basic types of brain waves were discussed, and exemplary states of brain activity were recorded. Particular attention was paid to technical aspects related to signal analysis. The LabVIEW environment was used to implement the created algorithm. The results of the research showing the operation of the developed EEG signal analyser were also presented.

Accumulation and Elimination: A Hard Decision-Based Multi-User Interference Cancellation Method in Satellite Communication System

With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (C/N0) resulting from long-distance transmission poses a significant concern. The parallel interference cancellation (PIC) algorithm, utilized within the paradigm of multi-user detection (MUD), exhibits the capability to effectively mitigate the impact of MAI within the same system. Simultaneously, coherent accumulation serves as a means to substantially enhance the correct detection probability (Pcd) at low C/N0. In this study, a signal acquisition method for multi-user spread spectrum satellite receivers is proposed, which employs interference cancellation and coherent accumulation as its core mechanisms. Furthermore, we introduce a power estimation method based on the outcomes of signal acquisition, which can be integrated into the signal reconstruction module of PIC. Finally, we implement the aforementioned algorithms in both simulation and hardware platforms. Remarkably, we observe that when the interference-to-signal ratio (ISR) caused by MAI equals 20 dB, the improved algorithm attains a maximum Pcd of 0.95 within the high signal-to-noise ratio (SNR) region, closely approaching the theoretical limit for the bit error rate (BER). The experimental results prove the effectiveness and feasibility of the acquisition algorithm. In summary, the enhanced algorithm holds vast potential for widespread implementation in multi-user spread spectrum communication systems.

Imaging the radioprotective effect of amifostine in the developing brain using an apoptosis-reporting transgenic zebrafish

Purpose Normal tissue radioprotectants alleviate radiation-induced damages and preserve critical organ functions. Investigating their efficacy in vivo remains challenging, especially in enclosed organs like the brain. An animal model that enables direct visualization of radiation-induced apoptosis while possessing the structural complexity of a vertebrate brain facilitates these studies in a precise and effective manner. Materials and methods We employed a secA5 transgenic zebrafish expressing secreted Annexin V fused with a yellow fluorescent protein to visualize radiation-induced apoptosis in vivo. We developed a semi-automated imaging method for standardized acquisition of apoptosis signals in batches of zebrafish larvae. Using these approaches, we studied the protective effect of amifostine (WR-2721) in the irradiated zebrafish larval brain. Results Upon 2 Gy total-body 137Cs irradiation, increased apoptosis could be visualized at high resolution in the secA5 brain at 2, 24, and 48 hour post irradiation (hpi). Amifostine treatment (4 mM) during irradiation reduced apoptosis significantly at 24 hpi and preserved Wnt active cells in the larval brain. When the 2 Gy irradiation was delivered in combination with cisplatin treatment (0.1 mM), the radioprotective effect of amifostine was also observed. Conclusions Our study reveals the radioprotective effect of amifostine in the developing zebrafish larval brain, and highlights the utility of secA5 transgenic zebrafish as a novel system for investigating normal tissue radioprotectants in vivo.

다중 GNSS 수신기를 위한 저전력 신호 획득 기법 설계 및 FPGA 구현

다중 GNSS 수신기를 위한 저전력 신호 획득 기법 설계 및 FPGA 구현

The Effects of Different Brain Regions on fNIRS-based Task-state Detection in Speech Imagery.

Brain-computer interface (BCI) based on speech imagery can decode users' verbal intent and help people with motor disabilities communicate naturally. Functional near-infrared spectroscopy (fNIRS) is a commonly used brain signal acquisition method. Asynchronous BCI can response to control commands at any time, which provides great convenience for users. Task state detection, defined as identifying whether user starts or continues covertly articulating, plays an important role in speech imagery BCIs. To better distinguish task state from idle state during speech imagery, this work used fNIRS signals from different brain regions to study the effects of different brain regions on task state detection accuracy. The imagined tonal syllables included four lexical tones and four vowels in Mandarin Chinese. The brain regions that were measured included Broca's area, Wernicke's area, Superior temporal cortex and Motor cortex. Task state detection accuracies of imagining tonal monosyllables with four different tones were analyzed. The average accuracy of four speech imagery tasks based on the whole brain was 0.67 and it was close to 0.69, which was the average accuracy based on Broca's area. The accuracies of Broca's area and the whole brain were significantly higher than those of other brain regions. The findings of this work demonstrated that using a few channels of Broca's area could result in a similar task state detection accuracy to that using all the channels of the brain. Moreover, it was discovered that speech imagery with tone 2/3 tasks yielded higher task state detection accuracy than speech imagery with other tones.

Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface.

Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. However, sub-scalp EEG has not yet been assessed for suitability in BCI applications. This study presents a preliminary comparison of visual evoked potentials (VEPs) recorded using sub-scalp and endovascular stent electrodes in a sheep. Sub-scalp electrodes recorded comparable VEP amplitude, signal-to-noise ratio and bandwidth to the stent electrodes.Clinical relevance-This is the first study to report a comparision between sub-scalp and stent electrode array signals. The use of sub-scalp EEG electrodes may aid in the long-term use of brain-computer interfaces.

High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed

This paper studies a method for high-precision acquisition of position signals for permanent magnet direct drive servo motors at low speed. First of all, the problem of poor position feedback accuracy and sensor feedback delay in the low-speed operation of the permanent magnet direct drive servo motor is analyzed. Secondly, through analysis and simulation, it is found that the interpolation method can play a certain role in compensating the rotor position signal. However, when the speed is close to 0, the output signal of the sensor will fluctuate in a short time, which will affect the speed control accuracy. Therefore, this paper uses the observer method to achieve high-precision acquisition of the position signal of the permanent magnet direct drive servo motor at low speed. The observer method adopts the idea of combining the system model and closed-loop control. Additionally, it makes full use of the parameter information of the motor system. The control performance of the motor can be better guaranteed through the design of the observer parameters and the accuracy of the rotor position estimation result has been greatly improved. Finally, an experimental platform for permanent magnet direct drive servo motors is built, and the rotor position signal acquisition method based on the observer method is verified to have good performance through simulation and experiments. Not only the accuracy of the rotor position estimation result is improved, but also the motor control performance is improved, realizing the stable operation of the permanent magnet direct drive servo motor at low speed.

A gait signal acquisition and parameter characterization method based on foot pressure detection combined with Azure Kinect system

The gait acquisition system can be used for gait analysis. The traditional wearable gait acquisition system will lead to large errors in gait parameters due to different wearing positions of sensors. The gait acquisition system based on marker method is expensive and needs to be used by combining with the force measurement system under the guidance of rehabilitation doctors. Due to the complex operation, it is inconvenient for clinical application. In this paper, a gait signal acquisition system that combines foot pressure detection and Azure Kinect system is designed. Fifteen subjects are organized to participate in gait test, and relevant data are collected. The calculation method of gait spatiotemporal parameters and joint angle parameters is proposed, and the consistency analysis and error analysis of the gait parameters of proposed system and camera marking method are carried out. The results show that the parameters obtained by the two systems have good consistency (Pearson correlation coefficient r ≥ 0.9, P < 0.05) and have small error (root mean square error of gait parameters is less than 0.1, root mean square error of joint angle parameters is less than 6). In conclusion, the gait acquisition system and its parameter extraction method proposed in this paper can provide reliable data acquisition results as a theoretical basis for gait feature analysis in clinical medicine.