Solving the inverse problem in electrocardiography imaging for atrial fibrillation using various time-frequency decomposition techniques based on empirical mode decomposition: A comparative study

Electrocardiographic imaging (ECGI) can aid in identifying the driving sources that cause and sustain atrial fibrillation (AF). Traditional regularization strategies for addressing the ECGI inverse problem are not currently concerned about the multi-scale analysis of the inverse problem, and these techniques are not clinically reliable. We have previously investigated the solution based on uniform phase mode decomposition (UPEMD-based) to the ECGI inverse problem. Numerous other methods for the time-frequency analysis derived from empirical mode decomposition (EMD-based) have not been applied to the inverse problem in ECGI. By applying many EMD-based solutions to the ECGI inverse problem and evaluating the performance of these solutions, we hope to find a more efficient EMD-based solution to the ECGI inverse problem. In this study, five AF simulation datasets and two real datasets from AF patients derived from a clinical ablation procedure are employed to evaluate the operating efficiency of several EMD-based solutions. The Pearson's correlation coefficient (CC), the relative difference measurement star (RDMS) of the computed epicardial dominant frequency (DF) map and driver probability (DP) map, and the distance (Dis) between the estimated and referenced most probable driving sources are used to evaluate the application of various EMD-based solutions in ECGI. The results show that for DF maps on all simulation datasets, the CC of UPEMD-based and improved UPEMD (IUPEMD)-based techniques are both greater than 0.95 and the CC of the empirical wavelet transform (EWT)-based solution is greater than 0.889, and the RDMS of UPEMD-based and IUPEMD-based approaches is less than 0.3 overall and the RDMS of EWT-based method is less than 0.48, performing better than other EMD-based solutions; for DP maps, the CC of UPEMD-based and IUPEMD-based techniques are close to 0.5, the CC of EWT-based is 0.449, and the CC of the remaining EMD-based techniques on the SAF and CAF is all below 0.1; the RDMS of UPEMD-based and IUPEMD-based are 0.06∼0.9 less than that of other EMD-based methods for all the simulation datasets overall. On two authentic AF datasets, the Dis between the first 10 real and estimated maximum DF positions of UPEMD-based and EWT-based methods are 212∼1440 less than that of others, demonstrating these two EMD-based solutions are superior and are suggested for clinical application in solving the ECGI inverse problem. On all datasets, EWT-based algorithms deconstruct the signal in the shortest time (no more than 0.12s), followed by UPEMD-based solutions (less than 0.81s), showing that these two schemes are more efficient than others.

An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA)

Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.

Investigating functional changes in the brain to intermittently induced auditory illusions and its relevance to chronic tinnitus

Several studies have demonstrated the neural correlates of chronic tinnitus. However, we still do not understand what happens in the acute phase. Past studies have established Zwicker tone (ZT) illusions as a good human model for acute tinnitus. ZT illusions are perceived following the presentation of a notched noise stimulus, that is, broadband noise with a narrow band-stop filter (notch). In the current study, we compared the neural correlates of the reliable perception of a ZT illusion to that which is not. We observed changes in evoked and total theta power in wide-spread regions of the brain particularly in the temporal-parietal junction, pregenual anterior cingulate cortex/ventromedial prefrontal cortex (pgACC/vmPFC), parahippocampus during perception of the ZT illusion. Furthermore, we observe that increased theta power significantly predicts a gradual positive change in the intensity of the ZT illusion. Such changes may suggest a malfunction of the sensory gating system that enables habituation to redundant stimuli and suppresses hyperactivity. It could also suggest a successful retrieval of the memory of the missing frequencies, resulting in their conscious perception indicating the role of higher-order processing in the mechanism of action of ZT illusions. To establish a more concrete relationship between ZT illusion and chronic tinnitus, future longitudinal studies following up a much larger sample of participants who reliably perceive a ZT illusion to see if they develop tinnitus at a later stage is essential. This could inform us if the ZT illusion may be a precursor to chronic tinnitus.

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Theta and Alpha Band Modulations Reflect Error-Related Adjustments in the Auditory Condensation Task

Error commission leads to adaptive adjustments in a number of brain networks that subserve goal-directed behavior, resulting in either enhanced stimulus processing or increased motor threshold depending on the nature of errors committed. Here, we studied these adjustments by analyzing post-error modulations of alpha and theta band activity in the auditory version of the two-choice condensation task, which is highly demanding for sustained attention while involves no inhibition of prepotent responses. Errors were followed by increased frontal midline theta (FMT) activity, as well as by enhanced alpha band suppression in the parietal and the left central regions; parietal alpha suppression correlated with the task performance, left central alpha suppression correlated with the post-error slowing, and FMT increase correlated with both behavioral measures. On post-error correct trials, left-central alpha band suppression started earlier before the response, and the response was followed by weaker FMT activity, as well as by enhanced alpha band suppression distributed over the entire scalp. These findings indicate that several separate neuronal networks are involved in post-error adjustments, including the midfrontal performance monitoring network, the parietal attentional network, and the sensorimotor network. Supposedly, activity within these networks is rapidly modulated after errors, resulting in optimization of their functional state on the subsequent trials, with corresponding changes in behavioral measures.