Phase Synchronization Research Articles

Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.

Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction

Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction

Phase synthesis method in multi-aperture optical systems based on iterative image processing algorithms

Background. Modern technologies in the field of optics and photonics place high demands on the quality and output power of the radiation source. The fulfillment of these requirements can currently be achieved by creating multi-aperture laser systems with coherent beam addition. The main problem of creating such systems is the development of phase synthesis methods in a multichannel optical system. Aim. In this paper, we consider a phase synchronization method without a reference beam for a coherent addition system with active feedback, which is based on the Gerchberg–Saxton algorithm. This algorithm makes it possible to reconstruct complex field amplitudes in the aperture and focal planes from intensity distributions in these fields. The application of this algorithm for multichannel systems is analyzed and its features, such as the occurrence of stagnation and ambiguity of the solution, are revealed. Methods. Solutions are proposed to eliminate the problem of convergence of the algorithm to side solutions and getting the iterative procedure into the local extremum using global optimization algorithms, methods of reducing the dimension of the problem and the introduction of antisymmetric amplitude modulation. Results. The paper demonstrates the results of phase field reconstruction for a large number of optical sources. For a seven-aperture system, a physical simulation was performed to restore phase information, confirming the results of numerical modeling. Conclusion. The proposed approach is a reasonable alternative to currently existing methods and can be used in the problem of coherent addition in multichannel optical systems.

Research on Phase Stabilization Algorithm of Femtosecond Timing System

This paper presents the design, implementation, and validation of a femtosecond timing system aimed at achieving precise time control and phase synchronization for large particle accelerators. A prototype system utilizing a continuous wave laser was developed, focusing on minimizing timing jitter and long-term phase drift. Key components include an optical delay line for coarse adjustments and a fiber stretcher for fine-tuning, achieving an adjustment precision of 1 femtosecond. The system incorporates a phase detection module with a non-In-phase/Quadrature downconversion approach, enabling high-accuracy phase measurements. A collaborative algorithm was designed to optimize the interplay between the optical delay line and the fiber stretcher, utilizing a proportional-integral-derivative (PID) control algorithm to enhance adjustment precision. A Field Programmable Gate Array (FPGA) served as the core interface converter, facilitating data communication and real-time phase information acquisition. Experimental results demonstrated significant improvements in phase stability, with average phase deviation reduced from 1374.104 fs to 15.782 fs, showcasing the effectiveness of the proposed system in achieving high precision and stability in phase control. This research provides a solid foundation for future advancements in timing systems for high-frequency reference signals.

Effects of continuous theta burst stimulation on contralateral primary motor cortex: a concurrent TMS-EEG study.

Continuous theta burst stimulation (cTBS) is a noninvasive brain stimulation technique. cTBS modulation is an effective treatment for motor dysfunction rehabilitation in post-stroke patients. However, there's currently a lack of research on the effects of cTBS stimulation on the contralesional hemisphere. To better understand the role of cTBS in motor rehabilitation, we investigated the neuroregulatory mechanisms of cTBS in the contralateral cortex using transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG). In this randomized, sham-controlled, single-blind study, 18 healthy subjects received two separate stimulation conditions: cTBS or sham stimulation applied to the left primary motor cortex (M1). TMS-EEG measurements were taken before and immediately after stimulation. We investigated the TMS-evoked potentials (TEPs), evoked oscillatory responses (EOR), and phase synchronization index (PSI) of TMS-EEG. The effects of cTBS were analyzed using two-way repeated-measures analysis of variance (RMANOVA). There was a significant "cTBS condition × time" interaction effect on the theta and gamma bands of EOR, and on interhemisphere PSI (inter-PSI) and global PSI in both cTBS stimulation conditions. (theta: F = 4.526, P = 0.041; gamma: F = 5.574, P = 0.024; inter-PSI: F = 5.028, P = 0.032; global PSI: F = 5.129, P = 0.030). After real cTBS modulation, the energy in the theta and gamma frequency bands was significantly higher than before (theta: F = 5.747, P = 0.022; gamma: F = 5.545, P = 0.024). The inter-PSI and global PSI significantly increased after real cTBS modulation (inter-PSI: F = 6.209, P = 0.018; global PSI: F = 6.530, P = 0.015). cTBS modulation significantly increased EOR and PSI in contralateral brain regions, thereby enhancing cortical excitability and cortical functional connectivity throughout the brain. This provides a theoretical basis for cTBS neuromodulation in patients with stroke.NEW & NOTEWORTHY In right-handed individuals, the left hemisphere exhibits higher excitability. According to hemispheric competition theory, applying continuous theta burst stimulation (cTBS) to inhibit excitability in the left hemisphere can reduce its inhibitory effect on the right, thereby promoting neural excitability. This study applied cTBS to the left M1 of healthy individuals and, for the first time, recorded transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG) from the right M1 to analyze the effects of cTBS on cortical oscillations and network connectivity in the contralateral cortex.

Pleasantness makes a good time: musical consonance shapes interpersonal synchronization in dyadic joint action.

Music making is a process by which humans across cultures come together to create patterns of sounds that are aesthetically pleasing. What remains unclear is how this aesthetic outcome affects the sensorimotor interaction between participants. Here we approach this question using an interpersonal sensorimotor synchronization paradigm to test whether the quality of a jointly created chord (consonant vs. dissonant) affects movement coordination. We recruited non-musician participants in dyads to perform a dyadic synchronization-continuation task (dSCT): on each trial, participants first synchronized their movements to a metronome (synchronization phase) and then continued tapping together at the same tempo without the metronome (continuation phase). Each tap yielded a note and participants heard both their own and that of their partner, thus creating a chord that was varied to be either consonant (Perf5 or Maj6) or dissonant (Min2 or Maj2). For each trial, participants also rated the pleasure they felt in creating the sounds together. Additionally, they completed questionnaires about social closeness to the other participant, musical reward sensitivity and musical training. Results showed that participants' taps were closer in time when they jointly created consonant (high pleasure) vs. dissonant (low pleasure) chords, and that pleasure experienced by the dyad in each trial predicted interpersonal synchronization. However, consonance did not affect individual synchronization with the metronome or individual tapping when the metronome was discontinued. The effect of consonance on synchronization was greater in dyads who reported feeling less close prior to the task. Together, these results highlight the role of consonance in shaping the temporal coordination of our actions with others. More broadly, this work shows that the aesthetic outcome of what we create together affects joint behaviors.

Effect of fatigue on intermuscular EMG-EMG coupling during bench press exercise at 60% 1RM workload in males.

To explore the neuromuscular control mechanism and quantifying the fatigue response during bench press exercise is important references to prescribe an appropriate exercise program. However, current literature struggles to provide a concrete conclusion on the changes of intermuscular EMG-EMG coupling between synergistic and antagonist muscles during the exercise. Thus, the current study was designed to reveal fatigue-related changes of intermuscular EMG-EMG coupling during bench press exercise. Thirty-one healthy male participants performed a bench press exercise on the Smith machine at 60% One Repetition Maximum (1RM) workload to exhaustion, while surface electromyographic signals (sEMG) were collected from triceps brachii (TB), biceps brachii (BB), anterior deltoid (AD), posterior deltoid (PD), and pectoralis major (PM). Surface EMG signals were divided into the first half and second half of the bench press exercise. Phase synchronization index (PSI) was calculated between sEMG of synergistic muscle pairs AD-TB, AD-PM and antagonist muscle pairs BB-TB, AD-PD. EMG power of TB, AD, PD, PM muscles in alpha (8-12 Hz) frequency band and EMG power of each muscle in beta (15-35 Hz), and gamma (35-60 Hz) frequency bands were all increased during the second half of contraction compared with the first half of contraction. PSI of gamma frequency band was significantly decreased in BB-TB muscle pair while EMG-EMG coupling of AD-TB in gamma frequency band was significantly increased during the second half of contraction compared to the first half of contraction. The results indicated a decrease of interconnection between synchronized cortical neurons and the motoneuron pool of BB and TB, and an increase of interconnection between AD-TB muscles during fatiguing bench press exercise at 60% 1RM workload. The changes of intermuscular coupling may be related to the supraspinal modulations to compensate for the decrease of muscle force as well as a result of unbalanced changes of agonist and antagonist muscle contractility.

EEG Oscillatory Activity and Resting-State Networks Associated with Neurocognitive Function in Mild Traumatic Brain Injury.

This study aimed to investigate the characteristics of resting-state electroencephalography (EEG) activity and brain networks in patients with mild traumatic brain injury (mTBI) and their association with neurocognitive function (NCF). We analyzed 26 patients with subacute mTBI and 21 healthy controls. The subacute mTBI group (9 females, 17 males) had a mean age of 29.9 ± 9.9 years, and the healthy controls (11 females, 10 males) had a mean age of 29.7 ± 11.5 years. Current source density, lagged phase synchronization, and resting-state network activity were analyzed using exact low-resolution electromagnetic tomography (eLORETA) with 60 s resting-state EEG data. In addition, a correlation analysis was performed between these EEG parameters and NCF in patients with mTBI. We used the statistical nonparametric mapping method in eLORETA to correct for multiple comparisons. There were no significant differences in EEG parameters between the patients with mTBI and healthy controls. However, in patients with mTBI, correlation analysis revealed negative correlations between theta activity in the anterior cingulate cortex and verbal short-term memory and between activity in the memory perception network and verbal memory. Our findings suggest that resting-state EEG may be clinically useful in investigating the mechanism of NCF decline in patients with mTBI.

Insights into a clock's fidelity through vesicular encapsulation.

The single-celled cyanobacterium, Synechococcus elongatus , generates circadian rhythms with exceptional fidelity and synchrony despite their femtoliter volumes. Here, we explore the mechanistic aspects of this fidelity, by reconstituting the KaiABC post-translational oscillator (PTO) in cell-mimetic giant vesicles (GUVs) under well-defined conditions in vitro . PTO proteins were encapsulated with a coefficient of variation that closely matched protein variations observed in live cells. Using fluorescently labeled KaiB and confocal microscopy, we were able to measure circadian rhythms generated by thousands of encapsulated PTOs at the single-vesicle level for several days as a function of protein concentration and GUV size. We find that PTO fidelity decreased with decreasing levels of encapsulated PTO proteins and in smaller GUVs. We also observed that in encapsulated PTOs, a significant fraction of KaiB localized to GUV membranes like it does in cyanobacteria. A mathematical model that uses empirical bulk concentration and stoichiometry limitations suggests that cyanobacteria overcome challenges to fidelity by expressing high levels of PTO proteins along with the CikA and SasA proteins, which buffer stochastic variations in the levels of KaiA and KaiB, respectively. Further, the model suggests that the transcription-translation feedback loop (TTFL) contributes at most a small percentage to the overall fidelity of the cyanobacterial circadian clock under constant conditions but is essential for maintaining phase synchrony. Our results are the first experimental demonstration of populations of synthetic cells that can autonomously keep circadian time. Additionally, the approach of using bulk relationships to understand complex phenomena in cell-like systems could be useful for understanding other collective behavior important in biology, such as liquid-liquid phase separation.

EEG synchronization patterns during a Go/No-Go task in individuals with aphasia in subacute and chronic phases of stroke

Stroke and subsequent neuroregenerative processes cause changes in neural organization of attentional functions. In this study, we attempted to identify differences in neural synchronization patterns during a visual Go/No-Go task in people with post-stroke aphasia in both subacute and chronic stroke phases. To identify neuronal underpinnings of the behavioral differences we investigated pairwise connectivity patterns using corrected imaginary phase locking value and graph-theoretic measures (efficiency, modularity and clustering coefficient) at global and local level in subacute (n = 13) and chronic stroke phases (n = 14) during a Go/No-Go task. We observed significantly lower phase synchronization in the Subacute Group in the alpha band in the connections spanning frontal and central areas of both hemispheres alongside lower local efficiency and clustering coefficient in the left frontal region. Additionally, we observed higher modularity in the beta band in the unaffected right parietal region in the Subacute Group which may denote inhibition of motor and attention functions. Those mechanisms could serve to align cognitive abilities between the damaged and healthy hemispheres, harmonizing the activity of the neuronal networks of both hemispheres disrupted by the effects of the stroke. Our findings have potential implications for rehabilitation therapies, which should take into account the pattern of connectivity changes during different phases of reovery.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Music Familiarization Elicits Functional Connectivity Between Right Frontal/Temporal and Parietal Areas in the Theta and Alpha Bands

Frequent listening to unfamiliar music excerpts forms functional connectivity in the brain as music becomes familiar and memorable. However, where these connections spectrally arise in the cerebral cortex during music familiarization has yet to be determined. This study investigates electrophysiological changes in phase-based functional connectivity recorded with electroencephalography (EEG) from twenty participants’ brains during thrice passive listening to initially unknown classical music excerpts. Functional connectivity is evaluated based on measuring phase synchronization between all pairwise combinations of EEG electrodes across all repetitions via repeated measures ANOVA and between every two repetitions of listening to unknown music with the weighted phase lag index (WPLI) method in different frequency bands. The results indicate an increased phase synchronization during gradual short-term familiarization between the right frontal and the right parietal areas in the theta and alpha bands. In addition, the increased phase synchronization is discovered between the right temporal areas and the right parietal areas at the theta band during gradual music familiarization. Overall, this study explores the short-term music familiarization effects on neural responses by revealing that repetitions form phasic coupling in the theta and alpha bands in the right hemisphere during passive listening.

Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

Phase Synchronism in the Generation of Harmonics in Gas Taking into Account Changes in the Wave Vector Detuning during the Propagation of an Intense Femtosecond Laser Pulse

Phase Synchronism in the Generation of Harmonics in Gas Taking into Account Changes in the Wave Vector Detuning during the Propagation of an Intense Femtosecond Laser Pulse

Cooperative Coherent Multistatic Imaging and Phase Synchronization in Networked Sensing

Cooperative Coherent Multistatic Imaging and Phase Synchronization in Networked Sensing

Effect of the magnitude of abrupt change in sound pressure on the magnitude and phase synchrony of 40-Hz auditory steady state response

A periodic sound with a fixed inter-stimulus interval elicits an auditory steady-state response (ASSR). An abrupt change in a continuous sound is known to affect the brain’s ongoing neural oscillatory activity, but the underlying mechanism has not been fully clarified. We investigated whether and how an abrupt change in sound intensity affects the ASSR. The control stimulus was a train of 1-ms clicks with a sound pressure level (SPL) of 70 dB at 40 Hz for 1000 ms. In addition to the control stimulus, we applied six stimuli with changes consisting of a 500-ms train at 70 dB followed by a 500-ms similar train with louder clicks of 75, 80, or 85 dB or weaker clicks of 55, 60, or 65 dB. We obtained the magnetoencephalographic responses from 15 healthy subjects while presenting the seven stimuli randomly. The two-dipole model obtained for the 40-Hz ASSR in the control condition was applied to all of the stimulus conditions for each subject, and then the time–frequency analysis was conducted. We observed that both the amplitude and the inter-trial phase coherence of the 40-Hz ASSR transiently decreased and returned to the steady state after the change onset, i.e., the desynchronization of 40-Hz ASSR. The degree of desynchronization depended on the magnitude of the change regardless of whether the sound intensity increased or decreased, which might be a novel neurophysiological index of cerebral response driven by a change in the sensory environment.

Altered electroencephalography-based source functional connectivity in drug-free patients with major depressive disorder

BackgroundCompared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD. MethodResting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity. ResultsCompared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band. LimitationsDue to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs. ConclusionsThe present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression.

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis

This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.

Synchronization of Phase and Frequency in Flyback Inverter Using a New Ripple Hysteresis Controller

Synchronization of Phase and Frequency in Flyback Inverter Using a New Ripple Hysteresis Controller