Individual Signals Research Articles

A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm

The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.

Conversational facial signals combine into compositional meanings that change the interpretation of speaker intentions

Human language is extremely versatile, combining a limited set of signals in an unlimited number of ways. However, it is unknown whether conversational visual signals feed into the composite utterances with which speakers communicate their intentions. We assessed whether different combinations of visual signals lead to different intent interpretations of the same spoken utterance. Participants viewed a virtual avatar uttering spoken questions while producing single visual signals (i.e., head turn, head tilt, eyebrow raise) or combinations of these signals. After each video, participants classified the communicative intention behind the question. We found that composite utterances combining several visual signals conveyed different meaning compared to utterances accompanied by the single visual signals. However, responses to combinations of signals were more similar to the responses to related, rather than unrelated, individual signals, indicating a consistent influence of the individual visual signals on the whole. This study therefore provides first evidence for compositional, non-additive (i.e., Gestalt-like) perception of multimodal language.

Lhx3/4 initiates a cardiopharyngeal-specific transcriptional program in response to widespread FGF signaling.

Individual signaling pathways, such as fibroblast growth factors (FGFs), can regulate a plethora of inductive events. According to current paradigms, signal-dependent transcription factors (TFs), such as FGF/MapK-activated Ets family factors, partner with lineage-determining factors to achieve regulatory specificity. However, many aspects of this model have not been rigorously investigated. One key question relates to whether lineage-determining factors dictate lineage-specific responses to inductive signals or facilitate these responses in collaboration with other inputs. We utilize the chordate model Ciona robusta to investigate mechanisms generating lineage-specific induction. Previous studies in C. robusta have shown that cardiopharyngeal progenitor cells are specified through the combined activity of FGF-activated Ets1/2.b and an inferred ATTA-binding transcriptional cofactor. Here, we show that the homeobox TF Lhx3/4 serves as the lineage-determining TF that dictates cardiopharyngeal-specific transcription in response to pleiotropic FGF signaling. Targeted knockdown of Lhx3/4 leads to loss of cardiopharyngeal gene expression. Strikingly, ectopic expression of Lhx3/4 in a neuroectodermal lineage subject to FGF-dependent specification leads to ectopic cardiopharyngeal gene expression in this lineage. Furthermore, ectopic Lhx3/4 expression disrupts neural plate morphogenesis, generating aberrant cell behaviors associated with execution of incompatible morphogenetic programs. Based on these findings, we propose that combinatorial regulation by signal-dependent and lineage-determinant factors represents a generalizable, previously uncategorized regulatory subcircuit we term "cofactor-dependent induction." Integration of this subcircuit into theoretical models will facilitate accurate predictions regarding the impact of gene regulatory network rewiring on evolutionary diversification and disease ontogeny.

Loss-Optimising-Management-Algorithm and the usage in an energy management for low-voltage grids

Since the amount of decentralised power generation is increasing it is important to develop an energy management system for low-voltage grids. This paper presents a method to operate such a management. The system is designed for managing a network of smart homes which can consume or supply electrical energy. The aims are to reduce the transmission losses and to stay within the limits of both, the voltage drop and the utilisation of lines and transformers. The reduction of the losses is implemented in the Loss-Optimising-ManagementAlgorithm (LOMA). This algorithm tries to find the power flow situation where minimal losses occur. The results of LOMA, the actual power situation (in low- and medium-voltage system) and the maximum power situation (based on the grid parameters) are summarised in an individual incentive signal for every smart home. The simulations show the feasibility of such an energy management and that significant loss reduction is achieved.

Short-term drought Index forecasting for hot and semi-humid climate Regions: A novel empirical Fourier decomposition-based ensemble Deep-Random vector functional link strategy

The development of advanced technologies based on computer aid models in the domain of crops and agriculture productively is a modern advancement. Machine learning (ML) based forecasting of the short-term drought indicators (e.g., Standardized Precipitation Evapotranspiration Index (SPEI)) based on individual signals is a complex process that involves several factors including data quality and availability, inherent signal complexity, non-stationarity of climate, and uncertainty in ML models. Recent achievements in the field of Fourier-based signal processing integrated with advanced deep learning approaches have made it possible to produce very accurate intelligent frameworks for multi-temporal drought indicators and fill this gap. In this research, a new ultramodern complementary intelligent framework comprised of the SelectKbest feature selection (FS), Empirical Fourier Decomposition (EFD), and deep ensemble random vector functional link (Deep RVFL) was developed for multi-temporal monthly forecasting of short-term drought indicators for three and six months (SPEI3 and SPEI6) for two different very hot and semi-humid climate zones of Iran.. For this purpose, the most influential time lags associated with each drought indicator were indicated using the SelectKbest FS in each zone. Afterwards, the individual SPEI signals were decomposed by the EFD technique imposing the most important lagged components to feed the ML approaches. Here, a new hybrid architecture deep learning model, namely a convolutional neural network coupled with a bidirectional gated recurrent unit (CNN-Bi-GRU) designed for multi-temporal drought forecasting in the next one-and three- months. Two advanced approaches, introducing the convolutional neural network coupled with bidirectional recurrent neural network (CNN-Bi-RNN), and Random vector function link (RVFL) were adopted to validate the main model (EFD-DeepRVFL) in complementary and standalone counterpart forms. The robustness of all the models was examined using several metrics such as coefficient of determination (R2), root mean square error (RMSE), reliability, and squared Chi-square Distance (SquD). The comprehensive assessment of the outcomes of hybrid schemes revealed that EFD-DeepRVFL owing to superior performance (R2|SPEI3(t + 1) = 0.953, R2|SPEI3(t + 3) = 0.837, R2|SPEI6(t + 1) = 0.962, and R2|SPEI6(t + 3) = 0.887) at Ahvaz station and (R2|SPEI3(t + 1) = 0.964, R2|SPEI3(t + 3) = 0.863, R2|SPEI6(t + 1) = 0.935, and R2|SPEI6(t + 3) = 0.839) at Kermanshah station outperformed the EFD-RVFL and CNN-Bi-RNN, respectively. The developed expert system provides early warning of drought conditions, as a decision-making tool, crop yield prediction, and water resources risk assessment.

Fault detection in automated production systems based on a long short-term memory autoencoder

Abstract In this paper, a hybrid model of regularized Long Short-Term Memory (LSTM) and autoencoder for fault detection in automated production systems is proposed. The presented LSTM autoencoder is used as a stochastic process model, which captures the normal behavior of a production system and allows to predict the probability distribution of sensor data. Discrepancies between the observed sensor data and the predicted probability density distribution are detected as potential faults. The approach combines the advantages of LSTMs and autoencoders: The correlations between individual sensor signals are exploited by an autoencoder, while the temporal dependencies are captured by LSTM neurons. A key challenge in training such a process model from historical data is to control the information passed through the latent space of the autoencoder. Different regularization methods are investigated for this purpose. Fault detection with the proposed LSTM autoencoder has been evaluated on the use case of an industrial penicillin production, achieving significantly improved results in comparison to the baseline LSTM.

Carbon-13 Radiofrequency Amplification by Stimulated Emission of Radiation of the Hyperpolarized Ketone and Hemiketal Forms of Allyl [1-13C]Pyruvate.

13C hyperpolarized pyruvate is an emerging MRI contrast agent for sensing molecular events in cancer and other diseases with aberrant metabolic pathways. This metabolic contrast agent can be produced via several hyperpolarization techniques. Despite remarkable success in research settings, widespread clinical adoption faces substantial roadblocks because the current sensing technology utilized to sense this contrast agent requires the excitation of 13C nuclear spins that also need to be synchronized with MRI field gradient pulses. Here, we demonstrate sensing of hyperpolarized allyl [1-13C]pyruvate via the stimulated emission of radiation that mitigates the requirements currently blocking broader adoption. Specifically, 13C Radiofrequency Amplification by Stimulated Emission of Radiation (13C RASER) was obtained after pairwise addition of parahydrogen to a pyruvate precursor, detected in a commercial inductive detector with a quality factor (Q) of 32 for sample concentrations as low as 0.125 M with 13C polarization of 4%. Moreover, parahydrogen-induced polarization allowed for the preparation of a mixture of ketone and hemiketal forms of hyperpolarized allyl [1-13C]pyruvate, which are separated by 10 ppm in 13C NMR spectra. This is a good model system to study the simultaneous 13C RASER signals of multiple 13C species. This system models the metabolic production of hyperpolarized [1-13C]lactate from hyperpolarized [1-13C]pyruvate, which has a similar chemical shift difference. Our results show that 13C RASER signals can be obtained from both species simultaneously when the emission threshold is exceeded for both species. On the other hand, when the emission threshold is exceeded only for one of the hyperpolarized species, 13C stimulated emission is confined to this species only, therefore enabling the background-free detection of individual hyperpolarized 13C signals. The reported results pave the way to novel sensing approaches of 13C hyperpolarized pyruvate, potentially unlocking hyperpolarized 13C MRI on virtually any MRI system─an attractive vision for the future molecular imaging and diagnostics.

Outage performance and energy efficiency optimization of wireless-powered millimeter-wave sensor networks

AbstractWith the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

MK-BMC: a Multi-Kernel framework with Boosted distance metrics for Microbiome data for Classification.

Research on human microbiome has suggested associations with human health, opening opportunities to predict health outcomes using microbiome. Studies have also suggested that diverse forms of taxa such as rare taxa that are evolutionally related and abundant taxa that are evolutionally unrelated could be associated with or predictive of a health outcome. Although prediction models were developed for microbiome data, no prediction models currently exist that use multiple forms of microbiome-outcome associations. We developed MK-BMC, a Multi-Kernel framework with Boosted distance Metrics for Classification using microbiome data. We propose to first boost widely used distance metrics for microbiome data using taxon-level association signal strengths to up-weight taxa that are potentially associated with an outcome of interest. We then propose a multi-kernel prediction model with one kernel capturing one form of association between taxa and the outcome, where a kernel measures similarities of microbiome compositions between pairs of samples being transformed from a proposed boosted distance metric. We demonstrated superior prediction performance of (i) boosted distance metrics for microbiome data over original ones and (ii) MK-BMC over competing methods through extensive simulations. We applied MK-BMC to predict thyroid, obesity, and inflammatory bowel disease status using gut microbiome data from the American Gut Project and observed much-improved prediction performance over that of competing methods. The learned kernel weights help us understand contributions of individual microbiome signal forms nicely. Source code together with a sample input dataset is available at https://github.com/HXu06/MK-BMC.

Assessing Protein Expression in Patient-Derived Xenografts Using Western Blotting.

The use of patient-derived xenografts (PDXs) in cancer research is increasing due to their ability to closely mimic the features of patient tumors. The ability to quickly and robustly measure protein expression levels in these tissues is a key methodology required in a broad range of experimental designs. Western blotting (WB) is a cost effective and simple tool that is highly specific and sensitive for detecting and quantifying individual proteins, posttranslational modifications and aberrant signaling pathways. Here, we described a method to assess protein expression in PDX tissues using WB to detect proteins involved in cell growth signaling pathways.

Recording media configurations for double-layered bit-patterned magnetic recording

The current magnetic recording technology is approaching its recording density limit. Recently, concepts focused on vertically stacking multiple recording media have gained momentum, driven by the potential to significantly expand recording density. The key challenge for such a system involves methods capable of efficiently separating data originating from the upper and lower layers within a single readback signal. To address this challenge, we first propose two distinct configurations of recording media in bit-patterned magnetic recording (BPMR) system with double recording layers. In both configurations, the centers of islands on the lower layer are positioned at the gaps between magnetic islands on the upper layer. This achieves the separation of the individual layer signals since the centers of islands in both layers are deliberately misaligned. Moreover, the equalizer design that takes the input data from both layers into consideration is also proposed. Simulation results demonstrate that when employing the same detector, the proposed configurations of the double-layered BPMR systems achieve better performance than the single-layered BPMR system.

Quantification of crystallinity during indomethacin crystalline transformation from α- to γ-polymorphic forms and of the thermodynamic contribution to dissolution in aqueous buffer and solutions of solubilizer.

The thermodynamic properties and dissolution of indomethacin (INM) were analyzed as models for poorly water-soluble drugs. Physical mixtures of the most stable γ-form and metastable α-form of INM at various proportions were prepared, and their individual signal intensities proportional to their mole fractions were observed using X-ray powder diffraction and Fourier transform infrared spectrometry at standard temperature. The endothermic signals of the α-form, with a melting point of 426 K, and that of the γ-form, with a melting point of 433 K, were obtained by differential scanning calorimetry (DSC). Furthermore, an exothermic DSC peak of the α/γ-phase transition at approximately 428 K was obtained. As we computed the melting entropy of the α-form and that of its transformation, the frequency of the transition was quantitatively determined, which indicated the maximum of the α/γ-phase transition at an α-form proportion of 68%. Subsequently, the thermodynamic contributions of the α- and γ-forms were analyzed using a Van't Hoff plot for solubility in aqueous solutions at pH 6.8. The dissolution enthalpies for α- and γ-forms were 28.2 and 31.2 kJ mol-1, respectively, which are in agreement with the quantitative contribution predicted by the product of the temperature and melting entropy. The contribution of melting entropy was conserved in different dissolution processes with aqueous solvents containing lidocaine, diltiazem, l-carnosine, and aspartame as solubilizers; their γ-form Setschenow coefficients were -39.6, +82.9, -17.3, and +23.2, whereas those of the α-form were -39.7, +80.4, -16.7, and +22.7, respectively. We conclude that the dissolution ability of the solid state and solubilizers indicate their additivity independently.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals.

Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

Traffic Light Detection and Recognition Using a Two-Stage Framework From Individual Signal Bulb Identification

Traffic Light Detection and Recognition Using a Two-Stage Framework From Individual Signal Bulb Identification

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions.

The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

Method for improving the quality of service of request signals in information systems

The work notes that the request information systems are an important component of information support for air traffic control and airspace control systems. Interrogative information systems include: secondary radar systems, national identification radar systems and short-range navigation radio systems. It is shown that the characteristics of the process of servicing request signals in an aircraft transponder have a significant impact on the statistical properties of the response signal flow, which ultimately, as a consequence, affects the efficiency of the entire radar channel for transmitting response signals with an active response. It is pointed out that one of the important characteristics of the process of servicing request signals in an aircraft transponder is its correlation function, based on the analysis of which one can conclude the choice of the type of response signal flow and the structure of the aircraft transponder. This makes it possible to achieve: increasing the probability of service, increasing the probability of detecting an air object, reducing the root-mean-square error in estimating the azimuth of an air object and increasing the reliability of the transmission of flight information from the observed board of an air object. The article analyzes the possibility of improving the quality of request service by managing the flow of request signals in a network of request information systems. It has been shown that the correct choice of repetition periods of request signals in the area of joint operation of request information systems makes it possible to reduce the likelihood of interaction between individual request signals. Also, as a consequence, it makes it possible to increase the noise immunity of the interrogative information systems under consideration that are part of the specified information system.

Machine learning based techniques for ECG noise removal and feature extraction

Machine learning (ML) is being applied to all aspects of life with the development of artificial intelligence (AI). This paper explores the application of machine learning technology in electrocardiogram (ECG) analysis to diagnose and classify a patient's current cardiac disease, predict possible future diseases, and provide a personalised treatment plan. However, several challenges have been highlighted. First, individual ECG signals display variability, causing concern about effective diagnosis based on changing ECG data. Second, different diseases can produce similar ECG results, requiring powerful classification algorithms to accurately classify diseases. Finally, using patient information to predict the probability of future heart attacks is critical to developing appropriate prevention and treatment strategies. Overcoming these challenges could revolutionise the field of cardiology. It could enable precise and proactive medical intervention. The study highlights the potential of machine learning to improve cardiovascular care and personalised medicine and emphasises the importance of addressing key challenges to maximise its impact in clinical practice.

Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition

Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features.

Novel PFAS-specific monitoring approach for highly impacted surface waters

In regulatory environmental monitoring programs, only a very small fraction of the vast number of per- and polyfluoroalkyl substances (PFAS) are investigated by target analysis. Therefore, non-target analysis (NTA) studies are increasingly conducted to detect unknown or unnoticed PFAS. These studies are often based on a few grab samples. Thus, discontinuously emitted PFAS from industrial batch processes might be easily overlooked. To address this deficiency and obtain in-depth information on the occurrence and temporal trend of PFAS in surface water impacted by treated industrial waste water, a comprehensive target and NTA study was implemented for 29 months.Elevated PFAS concentrations with up to 10.8 μg L−1 were detected in the river water by target analysis. In addition to PFAS target analysis, the water samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Data processing strategies and various filtering steps were applied to prioritize PFAS. Substances were identified by comparing data to available internal and external PFAS suspect lists, a fragment ion and neutral loss list, and spectral libraries. Several compounds were unequivocally identified based on reference standards.Fifty-five PFAS were (tentatively) identified using NTA. Of those, 43 could be assigned to 13 different homologous series. Partly fluorinated short-chain carboxylic acids (H-PFCA) and sulfonic acids (H-PFSA) were predominantly found in addition to perfluoroalkyl carboxylic acids (PFCA) and the alkyl ether carboxylic acid DONA. To the best of our knowledge, 12 PFAS were reported in surface water for the first time. Signal intensities of individual PFAS and signal ratios varied widely over time, which may indicate batch operations leading to discontinuous emission. Results and insights from this screening approach on PFAS can be used to optimize forthcoming surface water monitoring programs by including newly identified PFAS and selecting appropriate sampling intervals.

An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.