Temporal Domain Research Articles

Investigation of the reliability and validity of the Turkish version of the Comprehensive Coordination Scale in patients with Parkinson's disease

ObjectiveThe Comprehensive Coordination Scale (CCS) is a valid, reliable scale for evaluating motor coordination, described as the ability to produce context-dependent movements of multiple body parts in both spatial and temporal domains in patients with chronic stroke. The aim of this study was to develop a Turkish version of the CCS that can be used to evaluate coordination in Parkinson's disease patients and to test its validity and reliability. MethodsThirty-four patients with Parkinson’s disease (18 females, 16 males) with a mean age of 67.55± 10.93 years were included. The 9-Hole Peg Test (9 HPT), Timed Up and Go Test (TUG), Berg Balance Scale (BBS), Functional Reach Test (FRT), and Modified Hoehn and Yahr (H&Y) were administered to the patients. ResultsIn the correlation analysis for convergent validity, the CCS-TR showed a moderate negative correlation with both the 9 HPT and the TUG test (r=-0.684 and −0.641, respectively). There were also moderate positive correlations observed between the CCS-TR and the FRT and BBS (r=0.607 and 0.657, respectively). The total CCS-TR had excellent intrarater reliability (ICC=0.99; 95 % CI: 0.98–0.99) and interrater reliability (ICC=0.90; 95 % CI: 0.81–0.95). The CCS-TR subscales had excellent intrarater reliability (ICC: range 0.98 from 0.99). For intrarater agreement, the ICC was 0.99, and for interrater agreement, the ICC was 0.90 (excellent agreement). ConclusionThe results of the present study indicate that the Turkish version of the CCS is a reliable and valid coordination scale that can be used in patients with Parkinson’s disease.

EEG emotion recognition using EEG-SWTNS neural network through EEG spectral image

The rapid development in deep learning models provide considerable advancements in emotion recognition by electroencephalogram (EEG). However, existing approaches primarily focus on temporal and frequency domain features of EEG signals, neglecting spatial domain features in model information integration. Therefore, this study proposed a novel EEG input format, called EEG spectral image (ESI), which integrates spatial domain features using Azimuthal Equidistant Projection (AEP) and frequency domain features through differential entropy (DE). To better utilize this fine-grained data format, we propose the EEG Swin Transformer (EEG-SWTNS), which combines the window attention mechanism with shifted window partitioning. Window attention mechanism can concentrate on affective information within various regions and use shifted window partitioning to disrupt aggregated emotional representations for more granular features. Different from traditional graph neural networks, this method leverages the spatial locality of region information, leading to enhanced performance in EEG-based emotion recognition. Experiments conducted on SEED and SEED IV datasets demonstrate superior performance compared to baseline methods and the state-of-the-art models. Relative improvements of 0.6% and 0.08% are observed in subject-dependent experiments, while accuracies of 80.07% and 66.72% are achieved in subject-independent experiments without utilizing transfer learning techniques.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

Temporal self-supervised domain adaptation network for machinery fault diagnosis under multiple non-ideal conditions

Although deep learning techniques have been widely used in machinery fault diagnosis, the key issue of feature learning under multiple non-ideal conditions, i.e., strong noise interference, inconsistent data distributions and class imbalance, is not tackled effectively. Thus, a novel deep neural network (DNN) named temporal self-supervised domain adaptation network (TSSDAN) is developed for machinery fault diagnosis in this article. Firstly, a temporal self-supervised learning (TSSL) method is implemented to tackle the issue of strong noise interference and class imbalance. Secondly, a specific feature extractor, perception auto-encoder (PAE) is implemented for signal feature learning by mimicking the working mode of the biological visual system and spinal reflex system. Thirdly, a cascaded domain adaptation (CDA) method is constructed to tackle the domain shift issue by considering the feature distribution discrepancy of various domains for all perception levels. Two cases (i.e., rotor and gearbox) are used in this study to evaluate the performance of TSSDAN. The experiment results illustrate the outperformance of TSSDAN in solving the problem of deep feature learning and machinery fault diagnosis under multiple non-ideal conditions.

Emotion recognition in user‐generated videos with long‐range correlation‐aware network

AbstractEmotion recognition in user‐generated videos plays an essential role in affective computing. In general, visual information directly affects human emotions, so the visual modality is significant for emotion recognition. Most classic approaches mainly focus on local temporal information of videos, which potentially restricts their capacity to encode the correlation of long‐range context. To address this issue, a novel network is proposed to recognize emotions in videos. To be specific, a spatio‐temporal correlation‐aware block is designed to depict the long‐range correlations between input tokens, where the convolutional layers are used to learn the local correlations and the inter‐image cross‐attention is designed to learn the long‐range and spatio‐temporal correlations between input tokens. To generate diverse and challenging samples, a dual‐augmentation fusion layer is devised, which fuses each frame with its corresponding frame in the temporal domain. To produce rich video clips, a long‐range sampling layer is designed, which generates clips in a wide range of spatial and temporal domains. Extensive experiments are conducted on two challenging video emotion datasets, namely VideoEmotion‐8 and Ekman‐6. The experimental results demonstrate that the proposed method obtains better performance than baseline methods. Moreover, the proposed method achieves state‐of‐the‐art results on the two datasets. The source code of the proposed network is available at: https://github.com/JinChow/LRCANet.

A machine learning-based framework for spatio-temporal extension and filling of SMOS surface soil moisture observations over Canada

ABSTRACT Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products.

TMP: Temporal Motion Perception with spatial auxiliary enhancement for moving Infrared dim-small target detection

Infrared dim and small target detection has significant research value and application prospects in both military affairs and civil safety scenes. This topic presents substantial challenges due to the small size of the targets and their low contrast against the background in images. In the past ten years, most detection methodologies have primarily focused on single-frame images, mainly relying on spatial-domain image features to identify dim and small targets, with limited consideration of temporal-domain motion features. However, unlike general objects, the image features of dim-small targets are often easy to be interfered by background, and even seriously lost in capturing stages. These deficiencies can be the primary factors to impact the detection performance of single-frame methods. To promote infrared dim-small target detection, this paper proposes a novel multi-frame pipeline called Temporal Motion Perception with spatial auxiliary enhancement (TMP), which consists of two parallel feature extraction branches. One auxiliary branch is designed to capture traditional image features from spatial domain, and the other is proposed to extract the motion features of targets from temporal domain. In addition, a new Complementary Symmetry Weighting module is specially designed to fulfill the cross-domain fusion of spatio-temporal features, further enhancing feature representation. Experimental results on three public datasets demonstrate the efficiency of our proposed scheme. Compared with existing methods, it could often refresh the state-of-the-art (SOTA) performance on most metrics. Our codes are available at https://github.com/UESTC-nnLab/TMP.

Spreading Sea Clutter Suppression for High-Frequency Hybrid Sky-Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved.

Fluctuation in cortical excitation/inhibition modulates capability of attention across time scales ranging from hours to seconds.

Sustained attention, as the basis of general cognitive ability, naturally varies across different time scales, spanning from hours, e.g. from wakefulness to drowsiness state, to seconds, e.g. trial-by-trail fluctuation in a task session. Whether there is a unified mechanism underneath such trans-scale variability remains unclear. Here we show that fluctuation of cortical excitation/inhibition (E/I) is a strong modulator to sustained attention in humans across time scales. First, we observed the ability to attend varied across different brain states (wakefulness, postprandial somnolence, sleep deprived), as well as within any single state with larger swings. Second, regardless of the time scale involved, we found highly attentive state was always linked to more balanced cortical E/I characterized by electroencephalography (EEG) features, while deviations from the balanced state led to temporal decline in attention, suggesting the fluctuation of cortical E/I as a common mechanism underneath trans-scale attentional variability. Furthermore, we found the variations of both sustained attention and cortical E/I indices exhibited fractal structure in the temporal domain, exhibiting features of self-similarity. Taken together, these results demonstrate that sustained attention naturally varies across different time scales in a more complex way than previously appreciated, with the cortical E/I as a shared neurophysiological modulator.

Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems

Climate factors (CFs) are key variables shaping the interannual variability (IAV) of terrestrial ecosystem carbon sinks. However, the dominant CFs influencing the IAV of terrestrial carbon sinks remains debated, as CFs are coupled via land-atmosphere interactions. Here, the dominant factors influencing the IAV of global terrestrial net ecosystem production (NEP) were quantified using the convergent cross-mapping (CCM) technique. This analysis was conducted with distinct global terrestrial NEP datasets deriving from process-based ecosystem models, machine learning techniques, and eddy covariance flux towers. Results revealed that the spatial patterns of IAV of global terrestrial NEP were dominated by water availability (WA) and temperature (Ts). Ts mainly controlled the IAV of terrestrial NEP in mid to high-latitude regions of the Northern Hemisphere (NH), while WA exerted dominance over low and mid-latitude regions in both the NH and the Southern Hemisphere. Moreover, the energy limitation and water limitation explained the spatial pattern of Ts and WA dominant on NEP. Further analysis found that WA and Ts also played a dominant role in gross primary productivity (GPP) and terrestrial ecosystem respiration (TER), proving that WA and Ts were the dominant factors affecting NEP. In addition, we found a weakening trend in causal linkages of CFs to NEP in the temporal domain. This study used causal analysis to reveal the spatial patterns of water and heat dominating the NEP, providing support for improved assessment and prediction of terrestrial carbon sinks under climate change.

GaitNet+ARL: A Deep Learning Algorithm for Interpretable Gait Analysis of Chronic Ankle Instability.

Chronic ankle instability (CAI) is a major public health concern and adversely affects people's mobility and quality of life. Traditional assessment methods are subjective and qualitative by means of clinician observation and patient self-reporting, which may lead to inaccurate assessment and reduce the effectiveness of treatment in clinical practice. Gait analysis becomes a commonly used approach for monitoring human motion behaviors, which can be applied to specific diagnosis and assessment of CAI. However, it is still challenging to recognize the pathological gait pattern for CAI subjects. In this paper, we propose an integrated deep learning framework to solve the CAI recognition problem using kinematic data. Specifically, inspired by the biomechanics of human body system, we create a simple graph neural network (GNN), termed GaitNet, that operates on a spatial domain and exploits interactions among 3-D joint coordinates. We also develop an attention reinforcement learning (ARL) model that determines attention weights of frames on a temporal domain, which is combined with GaitNet for prediction. The effectiveness of our method is validated on the kinematic NEU-CAI dataset which is collected in our institution using a stereophotogrammetric system. According to extensive experiments, we demonstrate that the selected key phases (i.e., sequences of frames with high attentions) significantly increase the predictability of the proposed biomechanics-based GNN model to differentiate between CAI cohort and control cohort. Moreover, we show a significant prediction accuracy improvement (20%-25%) by our approach compared to state-of-the-art machine learning and deep learning methods.

AI approach on identifying change in public sentiment for major events: Dubai Expo 2020

Existing research in identifying changes in public sentiment for major events faces limitations in capturing the intricacies of temporal changes, especially with large-scale data and diverse linguistic nuances. This study addresses this problem, with a particular focus on the globally acclaimed Dubai Expo 2020. The methodology employed in this research introduces a groundbreaking approach by leveraging advanced data engineering techniques on a massive corpus of 118,025 Tweets obtained from diverse users across 60 languages, covering the period from April 2021 to March 2023. The study pioneers the application of artificial intelligence (AI), natural language processing (NLP), and Large Language Models (LLMs) for sentiment analysis and topic modeling on Twitter discourse. Through the meticulous data engineering process, including language detection, dynamic translation, and sentiment analysis, the research identifies subtle yet statistically significant changes in public sentiment, as evidenced by ANOVA testing (p=0.018 in average positive sentiment, p=0.004 in average neutral sentiment, and p=0.005 in average negative sentiments). Additionally, the study innovatively extracts and analyzes support-related Tweets, revealing distinct phases in temporal domains (pre-Expo, Expo, Post-Expo1, and Post-Expo2) and yielding 11,116 support-related tweets. The application of NLP techniques further uncovers 19 topics from UAE-related Tweets, providing a comprehensive understanding of the dynamic landscape of public sentiment over a two-year period. This research contributes significantly to the field by offering a novel and comprehensive framework for analyzing public sentiment, particularly in the context of major events, and sheds light on its broader implications for event management and public perception analysis.

An unsupervised damage detection method for an operating wind turbine blade

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.

Nonlinear Multimodal Interference as Ultrafast Photonic Device for Dual-Wavelength Domain-Wall Dark Pulse Generation

Abstract In comparison to bright pulses, better stability that is not susceptible to loss makes dark pulses accessible for applications in such fields as signal processing, optics sensing, and quantum communication. Here we investigate the dual-wavelength domain-wall dark pulse generation in a graded-index multimode fiber (GIMF) based anomalous dispersion single-mode fiber (SMF) laser. By optimizing intra-cavity nonlinearity and pulse polarization, the mode-locked states can evolve each other between bright pulses, dark pulses, and bright-dark pulse pairs. The evolution mechanism among them may be relevant to the coherent mode superposition, spectral filtering, and mode selection in SMF-GIMF-SMF hybrid-fiber modulation devices that affect the pulse formation and evolution in temporal, frequency, and space domains. These results provide a valuable reference for promoting further development of nonlinear optics and ultrafast optics, in which ultrafast photonic devices, with low cost, simple manufacture as well as wide adaptability, as novel pulsed generation technique, play a vital role.

Manipulating the Spatial Structure of Second-Order Quantum Coherence Using Entangled Photons

Abstract High-order quantum coherence reveals the statistical correlation of quantum particles. Manipulation of quantum coherence of light in the temporal domain enables the production of the single-photon source, which has become one of the most important quantum resources. High-order quantum coherence in the spatial domain plays a crucial role in a variety of applications, such as quantum imaging, holography, and microscopy. However, the active control of second-order spatial quantum coherence remains a challenging task. Here we predict theoretically and demonstrate experimentally the first active manipulation of second-order spatial quantum coherence, which exhibits the capability of switching between bunching and anti-bunching, by mapping the entanglement of spatially structured photons. We also show that signal processing based on quantum coherence exhibits robust resistance to intensity disturbance. Our findings not only enhance existing applications but also pave the way for broader utilization of higher-order spatial quantum coherence.

Probing the broad line region geometry and size of the gravitationally lensed quasar Q2237+0305 with microlensing time series

Lensed quasars are powerful cosmic laboratories; they are used to simultaneously probe various astrophysical phenomena. Microlensing by stars within distant galaxies acts as strong gravitational lenses of multiply imaged quasars, and provides a unique and direct measurement of the lensed quasar internal structure. Microlensing of the continuum emitting region as well as the broad-line region (BLR) is well characterized by four observable indices, μcont, μBLR, WCI (wing-core), and RBI (red-blue), measured directly from the spectra. During the 2004−2007 monitoring period, image A of the quadruply lensed system Q2237+0305 underwent a strong microlensing amplification, while image D remained unaffected. We used 35 epochs of archival spectrophotometric data of Q2237+0305 obtained with the Very Large Telescope of the European Southern Observatory to develop an independent microlensing method for estimating the geometry and size of the BLR. We measured the index time series for the C IV line and the continuum emission at 1450 Å. We built a library of the simulated microlensing index time series that reproduce the observed times series based on three representative BLR models: Keplerian disk (KD), polar wind (PW), and equatorial wind (EW). After sampling the model parameter space, we find that KD is the predominant model, while PW and EW are less likely. We infer that the system is viewed at an intermediate viewing angle i ∼ 35°, and we estimate the most likely C IV BLR half-light radius r1/2 = 51 ± 23 light days. Our results are in good agreement with previous findings in the literature and extend the validity of the index-based approach to a temporal domain.

Interfacial conditioning in physics informed neural networks

Physics informed neural networks (PINNs) have effectively demonstrated the ability to approximate the solutions of a system of partial differential equations (PDEs) by embedding the governing equations and auxiliary conditions directly into the loss function using automatic differentiation. Despite demonstrating potential across diverse applications, PINNs have encountered challenges in accurately predicting solutions for time-dependent problems. In response, this study presents a novel methodology aimed at enhancing the predictive capability of PINNs for time-dependent scenarios. Our approach involves dividing the temporal domain into multiple subdomains and employing an adaptive weighting strategy at the initial condition and at the interfaces between these subdomains. By employing such interfacial conditioning in physics informed neural networks (IcPINN), we have solved several unsteady PDEs (e.g., Allen–Cahn equation, advection equation, Korteweg–De Vries equation, Cahn–Hilliard equation, and Navier–Stokes equations) and conducted a comparative analysis with numerical results. The results have demonstrated that IcPINN was successful in obtaining highly accurate results in each case without the need for using any labeled data.

An Evaluation on the Effect of Water-Saving Renovation on a Large-Scale Irrigation District: A Case Study in the North China Plain

The construction of water-saving renovation projects can bring substantial benefits to the development of agriculture, but it may also be accompanied by negative impacts, especially in a large-scale irrigation district. Hence, there is always controversy, and it is vital and necessary to investigate the effectiveness of water-saving renovation. In this study, the Yahekou irrigation district, one of the largest districts in China, is selected as the case to explore the beneficial influence and adverse impact. Kriging interpolation, Pearson correlation analysis, and linear regression analysis are employed to study the temporal evolution, spatial distribution, and domain influencing factors. The results show that the water-saving renovation project in the Yahekou irrigation district had significant water-saving benefits during the period of 1998–2021, and the average annual water use of the irrigation district decreased by 61%. Canal lining is an important influencing factor for the decrease in irrigation water use, with a correlation coefficient of −0.538, B of −18.669, and R2 of 0.290. Furthermore, the water-saving renovation of irrigation districts is not the dominant reason for changes in groundwater depth. Meanwhile, the groundwater depth below ground level (the level DBGL) in the irrigation district increased by 82%. The combined effects of atmospheric precipitation, canal lining, river sand mining, and groundwater exploitation have led to a sustained increase in groundwater depth in the study area. The results obtained from this study can provide valuable and scientific reference for agricultural water resource management over the large-scale irrigation district. This article focuses on the impact of water-saving renovation on ecological and technical indicators such as water use and the groundwater DBGL. However, the impact of water-saving renovation in irrigation districts is multifaceted; subsequent research can explore the impact of water-saving renovation projects on society and the economy.

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Comment on “Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations” by D. Zhang, Y. Li, and S. Ying

Comment on “Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations” by D. Zhang, Y. Li, and S. Ying