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International Journal of Neural Systems

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https://www.readbyqxmd.com/read/28427290/a-real-time-method-for-decoding-the-neural-drive-to-muscles-using-single-channel-intra-muscular-emg-recordings
#1
Saeed Karimimehr, Hamid Reza Marateb, Silvia Muceli, Marjan Mansourian, Miguel Angel Mañanas, Dario Farina
The neural command from motor neurons to muscles - sometimes referred to as the neural drive to muscle - can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure...
March 20, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28420275/beta-hebbian-learning-as-a-new-method-for-exploratory-projection-pursuit
#2
Héctor Quintián, Emilio Corchado
In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback...
March 16, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28359222/online-automated-seizure-detection-in-temporal-lobe-epilepsy-patients-using-single-lead-ecg
#3
Thomas De Cooman, Carolina Varon, Borbála Hunyadi, Wim Van Paesschen, Lieven Lagae, Sabine Van Huffel
Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording...
February 16, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28359221/structured-pyramidal-neural-networks
#4
Alessandra M Soares, Bruno J T Fernandes, Carmelo J A Bastos-Filho
The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image...
February 9, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28355927/permutation-disalignment-index-as-an-indirect-eeg-based-measure-of-brain-connectivity-in-mci-and-ad-patients
#5
Nadia Mammone, Lilla Bonanno, Simona De Salvo, Silvia Marino, Placido Bramanti, Alessia Bramanti, Francesco C Morabito
OBJECTIVE: In this work, we introduce Permutation Disalignment Index (PDI) as a novel nonlinear, amplitude independent, robust to noise metric of coupling strength between time series, with the aim of applying it to electroencephalographic (EEG) signals recorded longitudinally from Alzheimer's Disease (AD) and Mild Cognitive Impaired (MCI) patients. The goal is to indirectly estimate the connectivity between the cortical areas, through the quantification of the coupling strength between the corresponding EEG signals, in order to find a possible matching with the disease's progression...
February 9, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28274168/resting-state-effective-connectivity-allows-auditory-hallucination%C3%A2-discrimination
#6
Manuel Graña, Leire Ozaeta, Darya Chyzhyk
Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex...
February 1, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27832712/visibility-graph-from-adaptive-optimal-kernel-time-frequency-representation-for-classification-of-epileptiform-eeg
#7
Zhong-Ke Gao, Qing Cai, Yu-Xuan Yang, Na Dong, Shan-Shan Zhang
Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states...
June 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27802792/changes-of-ionic-concentrations-during-seizure-transitions-a-modeling-study
#8
Damiano Gentiletti, Piotr Suffczynski, Vadym Gnatkovsky, Marco de Curtis
Traditionally, it is considered that neuronal synchronization in epilepsy is caused by a chain reaction of synaptic excitation. However, it has been shown that synchronous epileptiform activity may also arise without synaptic transmission. In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] ions, glial uptake and extracellular diffusion mechanisms...
June 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27785935/defense-against-chip-cloning-attacks-based-on-fractional-hopfield-neural-networks
#9
Yi-Fei Pu, Zhang Yi, Ji-Liu Zhou
This paper presents a state-of-the-art application of fractional hopfield neural networks (FHNNs) to defend against chip cloning attacks, and provides insight into the reason that the proposed method is superior to physically unclonable functions (PUFs). In the past decade, PUFs have been evolving as one of the best types of hardware security. However, the development of the PUFs has been somewhat limited by its implementation cost, its temperature variation effect, its electromagnetic interference effect, the amount of entropy in it, etc...
June 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27712455/independent-component-decomposition-of-human-somatosensory-evoked-potentials-recorded-by-micro-electrocorticography
#10
Irene Rembado, Elisa Castagnola, Luca Turella, Tamara Ius, Riccardo Budai, Alberto Ansaldo, Gian Nicola Angotzi, Francesco Debertoldi, Davide Ricci, Miran Skrap, Luciano Fadiga
High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction - not a negligible phenomenon - is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection...
June 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27712133/eeg-derived-brain-activity-reflects-treatment-response-from-vagus-nerve-stimulation-in-patients-with-epilepsy
#11
Simon Wostyn, Willeke Staljanssens, Leen De Taeye, Gregor Strobbe, Stefanie Gadeyne, Dirk Van Roost, Robrecht Raedt, Kristl Vonck, Pieter van Mierlo
The mechanism of action of vagus nerve stimulation (VNS) is yet to be elucidated. To that end, the effects of VNS on the brain of epileptic patients were studied. Both when VNS was switched "On" and "Off", the brain activity of responders (R, seizure frequency reduction of over 50%) was compared to the brain activity of nonresponders (NR, seizure frequency reduction of less than 50%). Using EEG recordings, a significant increase in P300 amplitude for R and a significant decrease in P300 amplitude for NR were found...
June 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27873554/a-realistic-seizure-prediction-study-based-on-multiclass-svm
#12
MULTICENTER STUDY
Bruno Direito, César A Teixeira, Francisco Sales, Miguel Castelo-Branco, António Dourado
A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729...
May 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27802791/an-ensemble-approach-for-cognitive-fault-detection-and-isolation-in-sensor-networks
#13
Manuel Roveri, Francesco Trovò
Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks...
May 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27785934/a-cross-correlated-delay-shift-supervised-learning-method-for-spiking-neurons-with-application-to-interictal-spike-detection-in-epilepsy
#14
Lilin Guo, Zhenzhong Wang, Mercedes Cabrerizo, Malek Adjouadi
This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance...
May 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27776438/independent-component-analysis-support-vector-machine-based-computer-aided-diagnosis-system-for-alzheimer-s-with-visual-support
#15
Laila Khedher, Ignacio A Illán, Juan M Górriz, Javier Ramírez, Abdelbasset Brahim, Anke Meyer-Baese
Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making...
May 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27760476/robust-wavelet-stabilized-footprints-of-uncertainty-for-fuzzy-system-classifiers-to-automatically-detect-sharp-waves-in-the-eeg-after-hypoxia-ischemia
#16
Hamid Abbasi, Laura Bennet, Alistair J Gunn, Charles P Unsworth
Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier...
May 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27596928/towards-tunable-consensus-clustering-for-studying-functional-brain-connectivity-during-affective-processing
#17
Chao Liu, Basel Abu-Jamous, Elvira Brattico, Asoke K Nandi
In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27440466/stress-detection-using-wearable-physiological-and-sociometric-sensors
#18
COMPARATIVE STUDY
Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Radu Dobrescu, Jose Manuel Ferrandez
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and [Formula: see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST)...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27440465/deep-learning-representation-from-electroencephalography-of-early-stage-creutzfeldt-jakob-disease-and-features-for-differentiation-from-rapidly-progressive-dementia
#19
MULTICENTER STUDY
Francesco Carlo Morabito, Maurizio Campolo, Nadia Mammone, Mario Versaci, Silvana Franceschetti, Fabrizio Tagliavini, Vito Sofia, Daniela Fatuzzo, Antonio Gambardella, Angelo Labate, Laura Mumoli, Giovanbattista Gaspare Tripodi, Sara Gasparini, Vittoria Cianci, Chiara Sueri, Edoardo Ferlazzo, Umberto Aguglia
A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept...
March 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27377661/sparse-bayesian-learning-for-obtaining-sparsity-of-eeg-frequency-bands-based-feature-vectors-in-motor-imagery-classification
#20
Yu Zhang, Yu Wang, Jing Jin, Xingyu Wang
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data...
March 2017: International Journal of Neural Systems
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