165 research outputs found

    Non-Propagating Components of Surface Electromyogram Reflect Motor Unit Firing Rates

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    Interference surface electromyogram (EMG) recorded from linear electrode arrays aligned to muscle fibres can be separated into propagating and non-propagating contributions. The first reflects the propagation of action potentials along muscle fibres. The non-propagating components are here shown to be related to the overall firing pattern of active motor units (MU). Indeed, in simulations, the power spectral density (PSD) of non-propagating components shows a low frequency peak corresponding to the mean firing rate, even when such a contribution is not visible in the PSD of the EMG (either monopolar or single differential configuration, either rectified or not). Moreover, it has a high correlation with the PSD of the cumulative firings of the MUs in the detection volume of the recording system. Applications to experimental data confirm that the low frequency peak is more evident for the non-propagating components than for the raw signals and is related to the MU firing frequency. Potential future applications are expected in the study of the MU control in different conditions (e.g., training, fatigue or pathology, inducing changes, or modulation of firing rate) and in the investigation of common synaptic inputs to motor neurons

    Inverse modelling to reduce crosstalk in high density surface electromyogram

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    Surface electromyogram (EMG) has a relatively large detection volume, so that it could include contributions both from the target muscle of interest and from nearby regions (i.e., crosstalk). This interference can prevent a correct interpretation of the activity of the target muscle, limiting the use of surface EMG in many fields. To counteract the problem, selective spatial filters have been proposed, but they reduce the representativeness of the data from the target muscle. A better solution would be to discard only crosstalk from the signal recorded in monopolar configuration (thus, keeping most information on the target muscle). An inverse modelling approach is here proposed to estimate the contributions of different muscles, in order to focus on the one of interest. The method is tested with simulated monopolar EMGs from superficial nearby muscles contracted at different force levels (either including or not model perturbations and noise), showing statistically significant improvements in information extraction from the data. The median over the entire dataset of the mean squared error in representing the EMG of the muscle under the detection electrode was reduced from 11.2% to 4.4% of the signal energy (5.3% if noisy data were processed); the median bias in conduction velocity estimation (from 3 monopolar channels aligned to the muscle fibres) was decreased from 2.12 to 0.72 m/s (1.1 m/s if noisy data were processed); the median absolute error in the estimation of median frequency was reduced from 1.02 to 0.67 Hz in noise free conditions and from 1.52 to 1.45 Hz considering noisy data

    Balanced multi-image demons for non-rigid registration of magnetic resonance images

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    A new approach is introduced for non-rigid registration of a pair of magnetic resonance images (MRI). It is a generalization of the demons algorithm with low computational cost, based on local information augmentation (by integrating multiple images) and balanced implementation. Specifically, a single deformation that best registers more pairs of images is estimated. All these images are extracted by applying different operators to the two original ones, processing local neighbors of each pixel. The following five images were found to be appropriate for MRI registration: the raw image and those obtained by contrast-limited adaptive histogram equalization, local median, local entropy and phase symmetry. Thus, each local point in the images is supplemented by augmented information coming by processing its neighbor. Moreover, image pairs are processed in alternation for each iteration of the algorithm (in a balanced way), computing both a forward and a backward registration. The new method (called balanced multi-image demons) is tested on sagittal MRIs from 10 patients, both in simulated and experimental conditions, improving the performances over the classical demons approach with minimal increase of the computational cost (processing time around twice that of standard demons). Specifically, a simulated deformation was applied to the MRIs (either original or corrupted by additive Gaussian or speckle noises). In all tested cases, the new algorithm improved the estimation of the simulated deformation (squared estimation error decreased by about 65% in the average). Moreover, statistically significant improvements were obtained in experimental tests, in which different brain regions (i.e., brain, posterior fossa and cerebellum) were identified by the atlas approach and compared to those manually delineated (in the average, Dice coefficient increased of about 6%). The conclusion is that a balanced method applied to multiple information extracted from neighboring pixels is a low cost approach to improve registration of MRIs

    Motor unit discharges from multi-kernel deconvolution of single channel surface electromyogram

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    Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference)

    Crosstalk in surface electromyogram: literature review and some insights

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    Surface electromyogram (EMG) has a relatively large pick-up volume, reflecting the activity of muscle tissue placed quite far from the electrodes. This could be beneficial when the global muscle activity is of interest, but it is a limitation when selective information is needed. The EMG from muscles that are neighbors of the one of interest is called crosstalk. Its interpretation, identification, quantification and removal have been the objectives of many works in the literature. However, it is still considered as an open problem, with effects that are difficult to predict. In this paper, the problem of crosstalk is discussed and the main literature is reviewed. Finally, a few recent techniques are introduced that are potentially relevant to quantify or reduce it

    Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies

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    Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub-and super-critical conditions (mean \ub1 std, ApEn about 0.93 \ub1 0.09, 0.66 \ub1 0.18, 0.49 \ub1 0.27, for the cultures in critical, sub-critical and super-critical state, respectively\u2014differences statistically significant). Our estimations were stable when considering epochs as short as 5 min (pairwise cross-correlation of spatial distribution of mean ApEn of 94 \ub1 5%). These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location

    Adversarial Neural Network Training for Secure and Robust Brain-to-Brain Communication

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    In the rapidly evolving domain of brain-to-brain communication, safeguarding the transmission of information against adversarial threats is paramount. This study introduces an advanced approach to enhance the resilience and security of brain-to-brain communication systems utilizing electroencephalogram data against such threats through adversarial neural network training. Concentrating on event-related potentials and employing a diverse collection of eight datasets, our research rigorously evaluates and optimizes the system's defense mechanisms against adversarial manipulations. We specifically target the optimization of trial durations and sampling rates to bolster system security. Our findings reveal a marked improvement in the system's defensive capabilities, demonstrated by a significant increase in adversarial accuracy by 17% and enhancement in the area under the receiver operating characteristic curve by 0.12 points. These results underscore the efficacy of our approach in fortifying brain-to-brain communication systems against sophisticated cyber threats, marking a significant step forward in the secure and robust transmission of neural signals
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