Signal Acquisition Techniques Research Articles

MI brain-computer interfaces: A concise overview

BCI stands for brain–computer interface, and this is the hottest topic among the research community to convert brain impulses into preset instructions that can be used to interact with each other or operate external equipment. A variety of BCI systems have been created during the last decades to give an alternate communications platform for people with extreme neurological illnesses including “amyotrophic lateral sclerosis, spinal cord damage, and brainstem stroke”. BCI literature reviews have also been produced to give BCI researchers well-organized information on important BCI research topics as well as valuable suggestions for the effective design of BCI systems. However, no one has lately looked into general trends in a range of EEG-based BCI study features. The inability of an EEG to measure neural activity down beyond the top layers of the brain is one of its limitations. Additionally, they have low signal-to-noise ratios and are useless for pinpointing the precise location of medications and neurotransmitters in the brain. In this paper, a literature review is conducted with 60 research appears collected from different international journals, published during different years (2013–2022). To be more valuable, this survey provides information about different aspects like signal acquisition techniques, EEG-based BCI paradigms, and channel selection techniques applied in the collected papers. A brief review of various techniques for feature extraction and classification is also included. In addition, the performance of each of the works in terms of accuracy, sensitivity, and kappa coefficient as well is manifest.

Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.

Multi-mode metasurface as a receive coil for magnetic resonance imaging

Sensitivity and diagnostic capabilities of magnetic resonance imaging (MRI) are dictated by the design of radio frequency (RF) receive coils. They must possess several key characteristics, such as a high signal-to-noise ratio (SNR), for image quality enhancement and multiple channels for scan time reduction. Artificial materials with tailored properties, such as metasurfaces, when used as RF coils, have been shown to deliver increased image quality or scan safety. Here, we suggest and describe a multi-mode metasurface-based coil with two independent receive channels. We experimentally show that such a coil provided a higher SNR than a state-of-the-art dedicated coil. We also demonstrate the feasibility of two intrinsic independent receive channels coexisting on the same frequency, enabling parallel signal acquisition techniques.

A programmable analog front-end with independent biasing technique for ECG signal acquisition

This paper presents a programmable low power and low noise analog front-end (AFE) for electrocardiogram (ECG) signal acquisition. A novel capacitor-coupled instrumentation amplifier (CCIA) features independent biasing technique and AC coupling to signal source is proposed. Independent biasing technique is capable of obtaining better noise performance and more stable static working point reducing the glitch of chopping. An AC coupling style helps suppress electrode DC offset and enhance input impedance with metal-oxide-metal capacitor array, which is symmetrical to the center to alleviate the mismatch of input capacitor enhancing system common mode rejection ratio (CMRR). The current steering technology (CST) and programmable feedback capacitors are utilized to achieve an area-efficient programmable low pass filter (PLPF) under PVT variations. Also, a charge buck after PLPF is introduced to strengthen the driving ability of AFE. To verify the proposed AFE structure, a prototype is designed in 0.13 μm standard CMOS process with a 1.2 V supply. Post-simulation results show that the input impedance reaches 1.4 GΩ under 50 Hz. The in-band white noise density and integrating noise are 48 nV/sqrt (Hz) and 0.6 μV, respectively. The obtained low pass corner of PLPF are 380 Hz, 451 Hz and 1 kHz. The CMRR and PSRR under montecarlo simulation are 93 dB and 77 dB, respectively. The power of whole AFE is 48 μW.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion.

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.

Compressed Sensing Approach for Physiological Signals: A Review

The immense progress in physiological signal acquisition and processing in health monitoring allowed a better understanding of patient disease detection and diagnosis. With the increase in data volume and power consumption, effective data compression, signal acquisition, transmission, and processing techniques are essential, especially in telemonitoring healthcare applications. An emerging research area focuses on integrating compressed sensing (CS) with physiological signals to deal with a massive amount of physiological data, transmission bandwidth, and power-saving purposes. A review of CS for physiological signals is presented in this article, including electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and electrodermal activity (EDA), focusing on the pros and cons of CS in treating such signals and the suitability of CS for hardware implementation. Furthermore, we emphasize performance matrices, such as compression ratio (CR), signal-to-noise ratio (SNR), Percentage Root-mean-square Difference (PRD), and processing time to evaluate the performance of CS. We also investigate the current practices, challenges, and opportunities of using CS in healthcare applications.

Impact of Antenna Pattern on TOA Based 3D UAV Localization Using a Terrestrial Sensor Network

In this article, we explore the fundamental limits of the 3-dimensional (3D) localization of unmanned aerial vehicles (UAVs) in conjunction with the effects of 3D antenna radiation patterns. Although localization of UAVs has been studied to some extent in the literature, effects of antenna characteristics on 3D localization remains mostly unexplored. To study such effects, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they derive the time-difference-of-arrival (TDOA) measurements from the time-of-arrival (TOA) data collected for the UAV that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAV, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAV altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAV heights, and propagation conditions for representative UAV scenarios.

Deep Adversarial Domain Adaptation With Few-Shot Learning for Motor-Imagery Brain-Computer Interface

Electroencephalography (EEG) is the most prevalent signal acquisition technique for brain-computer interface (BCI). However, the statistical distribution of EEG data varies across subjects and sessions, resulting in poor generalization of the domain-specific classifier. Although the collection of a large number of recordings may alleviate this issue, it is often impractical and not user-friendly. This study proposes the integration of deep domain adaptation with few-shot learning to address the challenge by leveraging the knowledge from multiple source subjects to enhance the performance of a single target subject. The framework incorporated 3 modules: a feature extractor, domain discriminator, and classifier. The feature extractor utilized the available labeled samples with supervised contrastive loss to map the discriminate features onto a deep representation space, where the features from the same class were more similar than those from different classes. The domain discriminator was used to reduce domain drift, through adversarial training. The classifier predicted the user motor intention, based on EEG features. The framework was extensively evaluated through the BCI Competition IV Datasets 2a and 2b. The results of this study indicate that the framework is capable of enhancing the BCI performance and potentially decreases the calibration effort compared to the traditional approach, but the major limitation of this method is that it requires meticulous selection of source subjects.

Early detection and risk control of safety signals of Chinese medicine

The safety of modern Chinese medicine has attracted increasing attention from society. Experts and scholars have carried out extensive in-depth research on the safety of commonly used Chinese medicines from various aspects such as safety monitoring, clinical research, and experimental analysis. The findings in the safety performance of Chinese medicines can inspire the mining and analysis of relevant signals in the drug safety alerts. A variety of methods are employed for the mining of risk signals or analysis and screening of relevant signals to accurately determine the correlation between medication and adverse reactions. The safety signal acquisition and mining techniques are the technical basis for the safety risk management of medicine products after Chinese medicines are marketed, which is critical in drug safety alerts. To accurately collect the safety signals of Chinese medicines and effectively and rapidly track, determine, and assess the sources of signals are important technical links in drug risk management. The ultimate purpose of safety signal discovery is to achieve normalized risk management through downgrading drugs from a high-risk level to a low-risk level.The five main steps in the standard drug risk management are listed below: to extensively collect predicted risk signals; to accurately identify drug risks by the techniques such as data mining; to evaluate the risks with process-based quality risk control; to employ management measures minimizing the impact of drug risks for risk management; to update and apply risk assessment to clinical evaluation after medication.

Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with e x tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.

Nucleation and growth in solution synthesis of nanostructures – From fundamentals to advanced applications

Nucleation and growth are two important and entwined processes in materials synthesis and engineering. While understanding of the fundamental mechanisms of the processes remain challenging, there is a growing demand for much improved control over and modification of nanostructures with precise geometrical and chemical features, well-tailored surface properties and functional attributes. To this end, we first examine the key concepts of the classical and non-classical theories and then emphasise mechanistic studies of nucleation and growth. Particularly, the state-of-the-art imaging, signal and/or data acquisition techniques are discussed, including in-situ liquid phase transmission electron microscopy, in-situ synchrotron X-ray diffraction, microfluidic platforms and machine learning. Both quantitative and qualitative experimental results with high temporal and spatial resolutions provide further insights into these nanofabrication processes for representative systems, such as Au nanoparticles and CaCO3, and subsequently guide the rational design and production of materials with desirable properties. Based on current knowledge, strategies of leveraging external fields to manipulate the nucleation and growth processes are presented. Several case studies in important technological scenarios are discussed to inspire further attempts for precisely engineered solutions. Finally, further understanding of the processes are highlighted along with potential applications and future perspectives of controlling solution-synthesized nanostructures.

A study on the usage of current signature for tool condition monitoring of drill bit

In the modern days manufacturing industry is shifting towards automation wherever possible. Most of the manufacturing operations like metal cutting, turning, milling, drilling etc., are being automated. Tool condition monitoring (TCM) plays a major role in automation that ensures the efficient use of tools thus saving lot of money being lost otherwise. It also saves lot of time because we will know in advance as to when a tool is going to break. Signal acquisition is one of the main and primary steps of TCM. Among various signal acquisition techniques current signal monitoring is the most economical method. Despite of the advantages in using current sensors, very limited research happened to predict remaining useful life (RUL) of a drill bit during drilling operation. Hence this work focuses on experimentally investigating the practical problems involved in employing current sensors for RUL prediction of drill bit. Most probable solutions for the problems faced are suggested in this paper. The outcome of this research is that it is possible to use ‘current sensors’ for TCM of Drill bit.

Review on electromyography signal acquisition and processing.

Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.

Optimization of parameters for improving the performance of EEG-based BCI system

Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.

SaS-BCI: a new strategy to predict image memorability and use mental imagery as a brain-based biometric authentication

Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication.

Review on Electromyography Signal Acquisition, Processing and Its Applications

Electromyography (EMG) signal is the type of biomedical signal, which is obtained from the neuromuscular activities. Typically, an electromyogram instrument is used to capture the EMG signals. These signals are used to monitor medical abnormalities, activation level, and also to analyze the biomechanics of any animal movements. In this current work, we provide a short review of EMG signal acquisition and processing techniques. We found that the average efficiency to capture EMG signals with the current technologies is around 70 %. Once the signal is captured, the signal processing algorithms applied decides the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g. moving robotic arm, speech recognition, gait analysis, etc). The recognition accuracy can go as high as 99.8 %. The accuracy with which the EMG signal is decoded has already crossed 99 %, and with the upcoming deep learning technology, there is a scope of improvement to design hardware, that can efficiently capture EMG signals.

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Surface electromyography (EMG) is non-invasive signal acquisition technique that plays a central role in many application, including clinical diagnostics, control for prosthetic devices and for human-machine interactions. The processing typically begins with a feature extraction step, which may be followed by the application of a dimensionality reduction technique. The obtained reduced features are input for a machine learning classifier. The constructed machine learning model may then classify new recorded movements.The features extracted for EMG signals usually capture information both from the time and from the frequency domain. Short time Fourier transform (STFT) is commonly used for signal processing and in particular for EMG processing since it captures the temporal and the frequency characteristics of the data. Since the number of calculated STFT features is large, a common approach in signal processing and machine learning applications is to apply a linear or a nonlinear dimensionality reduction technique for simplifying the feature space. Another aspect that arises in medical applications in general and in EMG based hand classification in particular, is the large variability between subjects. Due to this variability, many studies focus on single subject classification. This requires acquiring a large training set for each tested participant which is not practical in real life application.The objectives of this study were first to compare between the performances of a nonlinear dimensionality technique to a standard linear dimensionality method when applied for single subject EMG based hand movement classification, and to examined their performances in case of limited amount of training data samples. The second objective was to propose an algorithm for multi-subjects classification that utilized a data alignment step for overcoming the large variability between subjects.The data set included EMG signals from 5 subjects who perform 6 different hand movements. STFT was calculated for feature extraction, principal component analysis (PCA) and diffusion maps (DM) were compared for dimension reductions. An affine transformation for aligning between the reduced feature spaces of two subjects, was investigated. K-nearest neighbors (KNN) was used for single and multi-subject classification.The results of this study clearly show that the DM outperformed the PCA in case of limited training data. In addition, the multi-subject classification approach, which utilizes dimension reduction methods along with an alignment algorithm enable robust classification of a new subject based on another subjects’ data sets. The proposed framework is general and can be adopted for many EMG classification task.

Analysis of efficient unmanned aerial vehicles to handle medical emergency data transmission surveillance system by using wireless body area network

The Wireless Body Area Network (WBAN) gathers medical data from remote patients using wireless sensor nodes and transmits to the physician or server. The allocation of bandwidth to all network nodes demands the priority for communication because the high priority information presupposes people’s lives. The reduced propagation delay or nearly real-time transmission environment is necessary for top concern or chronic illness systems to transmit data from patients to the care worker comparing healthy patients. In indoor Wireless communication, particularly in hospitals, there is a risk of a significant packet error or failure during transmission. Based on the requirements of the studies, the dynamic cycle-based signal acquisition and bandwidth allocation technique created to reduce the emergency node propagation delay and enhance the signal quality of an emergency node. First, a patient’s priority-based data acquisition and sampling approach completed. Second, the calculation of duty cycle-based bandwidth requirements for non-emergency nodes made. Finally, a dynamic scheduling approach to the allocation of bandwidth using a regression method introduced. In this study, to regulate or handle a significant quantity of wireless node (1000 nodes, the abstract Software Defined Network (SDN) in the WBAN setting used. The controller monitors the network priority based on assigning the bandwidth to the particular nodes using the regression analysis technique.

Codeless Acquisition of GNSS Signals

Theoretical characteristics of codeless GPS-signal acquisition in the L1 band are described and analyzed. The results obtained allow for a reliable choice of a coherent integration interval, calculation of signal detection thresholds, and estimation of signal detection probability. The operability and feasibility of the technique for codeless signal acquisition by frequency has been experimentally confirmed with the use of a software receiver.