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International Journal of Neural Systems

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https://www.readbyqxmd.com/read/28691561/quantification-of-graph-complexity-based-on-the-edge-weight-distribution-balance-application-to-brain-networks
#1
Javier Gomez-Pilar, Jesús Poza, Alejandro Bachiller, Carlos Gómez, Pablo Núñez, Alba Lubeiro, Vicente Molina, Roberto Hornero
The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the 'information' (calculated by means of Shannon entropy) and the 'order' of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution...
May 23, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28659042/announcement-the-2017-hojjat-adeli-award-for-outstanding-contributions-in-neural-systems
#2
(no author information available yet)
No abstract text is available yet for this article.
September 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28587498/surface-eeg-transcranial-direct-current-stimulation-tdcs-closed-loop-system
#3
Jorge Leite, Leon Morales-Quezada, Sandra Carvalho, Aurore Thibaut, Deniz Doruk, Chiun-Fan Chen, Steven C Schachter, Alexander Rotenberg, Felipe Fregni
Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS...
September 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
#4
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...
August 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28274168/resting-state-effective-connectivity-allows-auditory-hallucination-discrimination
#5
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...
August 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28178853/clinical-vagus-nerve-stimulation-paradigms-induce-pronounced-brain-and-body-hypothermia-in-rats
#6
Lars Emil Larsen, Wouter Van Lysebettens, Charlotte Germonpré, Sofie Carrette, Sofie Daelemans, Mathieu Sprengers, Lisa Thyrion, Wytse Jan Wadman, Evelien Carrette, Jean Delbeke, Paul Boon, Kristl Vonck, Robrecht Raedt
Vagus nerve stimulation (VNS) is a widely used neuromodulation technique that is currently used or being investigated as therapy for a wide array of human diseases such as epilepsy, depression, Alzheimer's disease, tinnitus, inflammatory diseases, pain, heart failure and many others. Here, we report a pronounced decrease in brain and core temperature during VNS in freely moving rats. Two hours of rapid cycle VNS (7s on/18s off) decreased brain temperature by around [Formula: see text]C, while standard cycle VNS (30[Formula: see text]s on/300[Formula: see text]s off) was associated with a decrease of around [Formula: see text]C...
August 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27873553/real-time-control-of-an-exoskeleton-hand-robot-with-myoelectric-pattern-recognition
#7
Zhiyuan Lu, Xiang Chen, Xu Zhang, Kay-Yu Tong, Ping Zhou
Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion...
August 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27873552/correlated-eeg-signals-simulation-based-on-artificial-neural-networks
#8
Nikola M Tomasevic, Aleksandar M Neskovic, Natasa J Neskovic
In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence)...
August 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27873551/on-the-methodological-implications-of-extracting-muscle-synergies-from-human-locomotion
#9
Alessandro Santuz, Antonis Ekizos, Lars Janshen, Vasilios Baltzopoulos, Adamantios Arampatzis
We investigated the influence of three different high-pass (HP) and low-pass (LP) filtering conditions and a Gaussian (GNMF) and inverse-Gaussian (IGNMF) non-negative matrix factorization algorithm on the extraction of muscle synergies from myoelectric signals during human walking and running. To evaluate the effects of signal recording and processing on the outcomes, we analyzed the intraday and interday computation reliability. Results show that the IGNMF achieved a significantly higher reconstruction quality and on average needs one less synergy to sufficiently reconstruct the original signals compared to the GNMF...
August 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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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/28633550/right-fronto-temporal-eeg-can-differentiate-the-affective-responses-to-award-winning-advertisements
#20
Regina W Y Wang, Shy-Peih Huarng, Shang-Wen Chuang
Affective engineering aims to improve service/product design by translating the customer's psychological feelings. Award-winning advertisements (AAs) were selected on the basis of the professional standards that consider creativity as a prerequisite. However, it is unknown if AA is related to satisfactory advertising performance among customers or only to the experts' viewpoints towards the advertisements. This issue in the field of affective engineering and design merits in-depth evaluation. We recruited 30 subjects and performed an electroencephalography (EEG) experiment while watching AAs and non-AAs (NAAs)...
April 28, 2017: International Journal of Neural Systems
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