Collaborative adaptive filtering for machine learning

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data

FMRI-Based Static and Dynamic Functional Connectivity Analysis for Post-Stroke Motor Dysfunction Patient:A Review

Functional magnetic resonance imaging (fMRI) has emerged as a prevalent tool for investigating motor deficits and rehabilitation in the context of stroke. Particularly, the exploration of functional connectivity (FC) through resting-state fMRI has the potential to unveil the neural connectivity mechanisms underlying post-stroke motor impairment and recovery. Despite the significance of this approach, there exists a gap in the literature where a comprehensive review dedicated to post-stroke functional connectivity analysis is lacking. In this paper, we undertake an extensive review of both static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) in the context of post-stroke motor dysfunction. Our primary goal is to present comprehensive methodological insights and the latest research findings pertaining to motor function recovery after stroke. We commence by providing a succinct overview of SFC and DFC methods, elucidating the preprocessing and denoising techniques essential to these analyses. Subsequently, we summarize the application of two methods in stroke disease, mainly focusing on the extracted insight into post-stroke brain dysfunction and rehabilitation. Our review indicates a prevalence of SFC as the method of choice for post-stroke functional connectivity investigations. Specifically, SFC studies reveal a reduction in FC between motor areas due to stroke lesions, with increased FC correlating positively with functional recovery. Nevertheless, the DFC for post-stroke analysis has only begun to unveil its potential due to its ability in temporal dynamics. In summary, this review paper presents a thorough understanding of post-stroke functional connectivity analysis and its implications for the study of motor function recovery, offering valuable insights for future research and clinical applications.</p

Dynamic Reconfiguration of Brain Functional Network in Stroke

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer network method, a dynamic modular structure was recognized based on a time-resolved function network. Then dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, to reveal the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It is also noted that this severity-dependent alteration in network interaction was not able to be revealed by previous studies using static methods. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain signal could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The proposed method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction

Modelling Noninvasively Measured Cerebral Signals during a Hypoxemia Challenge: Steps towards Individualised Modelling

Noninvasive approaches to measuring cerebral circulation and metabolism are crucial to furthering our understanding of brain function. These approaches also have considerable potential for clinical use “at the bedside”. However, a highly nontrivial task and precondition if such methods are to be used routinely is the robust physiological interpretation of the data. In this paper, we explore the ability of a previously developed model of brain circulation and metabolism to explain and predict quantitatively the responses of physiological signals. The five signals all noninvasively-measured during hypoxemia in healthy volunteers include four signals measured using near-infrared spectroscopy along with middle cerebral artery blood flow measured using transcranial Doppler flowmetry. We show that optimising the model using partial data from an individual can increase its predictive power thus aiding the interpretation of NIRS signals in individuals. At the same time such optimisation can also help refine model parametrisation and provide confidence intervals on model parameters. Discrepancies between model and data which persist despite model optimisation are used to flag up important questions concerning the underlying physiology, and the reliability and physiological meaning of the signals

Dynamic Reconfiguration of Brain Functional Network in Stroke

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer network method, a dynamic modular structure was recognized based on a time-resolved function network. Then dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, to reveal the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It is also noted that this severity-dependent alteration in network interaction was not able to be revealed by previous studies using static methods. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain signal could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The proposed method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction

Dynamic Reconfiguration of Brain Functional Network in Stroke

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.</p

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes

Objectives: Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Design: Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Results: Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. Conclusions: This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.</p

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

The concept of muscle synergies has proven to be an effective method for representing patterns of muscle activation. The number of degrees of freedom to be controlled are reduced while also providing a flexible platform for producing detailed movements using synergies as building blocks. It has previously been shown that small components of movement are crucial to producing precise and coordinated movement. Methods which focus on the variance of the data make it possible to overlook these small components in the synergy extraction process. However, algorithms which address the inherent complexity in the neuromuscular system are lacking. To that end we propose a new nonnegative matrix factorization algorithm which employs a cross fuzzy entropy similarity measure, thus, extracting muscle synergies which preserve the complexity of the recorded muscular data. The performance of the proposed algorithm is illustrated on representative EMG data

Collaborative adaptive filtering in the complex domain

A novel hybrid filter combining the complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms for complex domain adaptive filtering is introduced. The ACLMS has been shown to have improved performance in terms of prediction of non–circular complex data compared to that of the CLMS. By taking advantage of this along with the faster convergence of the CLMS, the hybrid filter is shown to give improved performance over both algorithms for both cir-cular and non–circular data. Simulations on complex–valued synthetic and real world data support the effectiveness of this approach. 1