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Impact of Machine Learning Pipeline Choices in Autism Prediction From Functional Connectivity Data Int. J. Neural Syst. (IF 5.604) Pub Date : 2021-01-20 Manuel Graña; Moises Silva
Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest
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Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study Int. J. Neural Syst. (IF 5.604) Pub Date : 2021-01-12 John Thomas; Prasanth Thangavel; Wei Yan Peh; Jin Jing; Rajamanickam Yuvaraj; Sydney S. Cash; Rima Chaudhari; Sagar Karia; Rahul Rathakrishnan; Vinay Saini; Nilesh Shah; Rohit Srivastava; Yee-Leng Tan; Brandon Westover; Justin Dauwels
The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated
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The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm Int. J. Neural Syst. (IF 5.604) Pub Date : 2021-01-12 Ying Mao; Jing Jin; Ren Xu; Shurui Li; Yangyang Miao; Andrzej Cichocki
Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached
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Improving Botulinum Toxin Efficiency in Treating Post-Stroke Spasticity Using 3D Innervation Zone Imaging Int. J. Neural Syst. (IF 5.604) Pub Date : 2021-01-12 Chuan Zhang; Yen-Ting Chen; Yang Liu; Elaine Magat; Monica Gutierrez-Verduzco; Gerard E. Francisco; Ping Zhou; Sheng Li; Yingchun Zhang
Spasticity is a common post-stroke syndrome that imposes significant adverse impacts on patients and caregivers. This study aims to improve the efficiency of botulinum toxin (BoNT) in managing spasticity, by utilizing a three-dimensional innervation zone imaging (3DIZI) technique based on high-density surface electromyography (HD-sEMG) recordings. Stroke subjects were randomly assigned to two groups:
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LieToMe: An Ensemble Approach for Deception Detection from Facial Cues Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-16 Danilo Avola; Marco Cascio; Luigi Cinque; Alessio Fagioli; Gian Luca Foresti
Deception detection is a relevant ability in high stakes situations such as police interrogatories or court trials, where the outcome is highly influenced by the interviewed person behavior. With the use of specific devices, e.g. polygraph or magnetic resonance, the subject is aware of being monitored and can change his behavior, thus compromising the interrogation result. For this reason, video analysis-based
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On the Modeling of the Three Types of Non-spiking Neurons of the Caenorhabditis elegans Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-03 Loïs Naudin; Nathalie Corson; M. A. Aziz-Alaoui; Juan Luis Jiménez Laredo; Thibaut Démare
The nematode Caenorhabditis elegans (C. elegans) is a well-known model organism in neuroscience. The relative simplicity of its nervous system, made up of few hundred neurons, shares some essential features with more sophisticated nervous systems, including the human one. If we are able to fully characterize the nervous system of this organism, we will be one step closer to understanding the mechanisms
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Evolution-Communication Spiking Neural P Systems Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-07 Tingfang Wu; Qiang Lyu; Linqiang Pan
Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons
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Dendrite P Systems Toolbox: Representation, Algorithms and Simulators Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-16 David Orellana-Martín; Miguel Á. Martínez-del-Amor; Luis Valencia-Cabrera; Ignacio Pérez-Hurtado; Agustín Riscos-Núñez; Mario J. Pérez-Jiménez
Dendrite P systems (DeP systems) are a recently introduced neural-like model of computation. They provide an alternative to the more classical spiking neural (SN) P systems. In this paper, we present the first software simulator for DeP systems, and we investigate the key features of the representation of the syntax and semantics of such systems. First, the conceptual design of a simulation algorithm
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Acrophobia Quantified by EEG Based on CNN Incorporating Granger Causality Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-23 Fo Hu; Hong Wang; Qiaoxiu Wang; Naishi Feng; Jichi Chen; Tao Zhang
The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides
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The Effect of Low Magnesium Concentration on Ictal Discharges In A Non-Synaptic Model Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-23 Antônio Márcio Rodrigues; Delmo Benedito Silva; Maísa Ferreira Miranda; Silvia Cristina Braga da Silva; Luiz Eduardo Canton Santos; Fulvio Alexandre Scorza; Carla Alessandra Scorza; Marcelo A. Moret; Antônio-Carlos Guimarães de Almeida
Magnesium (Mg2+) is an essential mineral for several cellular functions. The concentration of this ion below the physiological concentration induces recurrent neuronal discharges both in slices of the hippocampus and in neuronal cultures. These epileptiform discharges are initially sensitive to the application of N-methyl-D-aspartate (NMDA) receptor antagonists, but these antagonists may lose their
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Roles of Very Fast Ripple (500–1000Hz) in the Hippocampal Network During Status Epilepticus Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-23 Jianmin Hao; Yan Cui; Bochao Niu; Liang Yu; Yuhang Lin; Yang Xia; Dezhong Yao; Daqing Guo
Very fast ripples (VFRs, 500–1000Hz) are considered more specific than high-frequency oscillations (80–500Hz) as biomarkers of epileptogenic zones. Although VFRs are frequent abnormal phenomena in epileptic seizures, their functional roles remain unclear. Here, we detected the VFRs in the hippocampal network and tracked their roles during status epilepticus (SE) in rats with pilocarpine-induced temporal
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Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-22 Parvaneh Rashvand; Mohammad Reza Ahmadzadeh; Farzaneh Shayegh
In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they
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Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-22 Yongjie Zhu; Xiaoyu Wang; Klaus Mathiak; Petri Toiviainen; Tapani Ristaniemi; Jing Xu; Yi Chang; Fengyu Cong
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening
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Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-12-22 Pasquale Arpaia; Francesco Donnarumma; Antonio Esposito; Marco Parvis
A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI
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Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-25 Antonio Maria Chiarelli; Pierpaolo Croce; Giovanni Assenza; Arcangelo Merla; Giuseppe Granata; Nadia Mariagrazia Giannantoni; Vittorio Pizzella; Franca Tecchio; Filippo Zappasodi
Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying
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Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-10-09 Camillo Porcaro; Stephen D. Mayhew; Marco Marino; Dante Mantini; Andrew P. Bagshaw
Intrinsic brain activity is organized into large-scale networks displaying specific structural–functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension
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Spiking Neural P Systems with Astrocytes Producing Calcium Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-18 Bogdan Aman; Gabriel Ciobanu
The astrocytes are cells which play an essential role in the functioning and interaction of neurons by feeding the respective neurons with calcium ions. Drawing inspiration from this two-way relationship in which the astrocytes influence and are influenced by the neurons by means of calcium ions, in this paper, we define and study spiking neural P systems with astrocytes producing calcium. Distinct
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Dynamic Temporospatial Patterns of Functional Connectivity and Alterations in Idiopathic Generalized Epilepsy Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-07 Sisi Jiang; Haonan Pei; Yang Huang; Yan Chen; Linli Liu; Jianfu Li; Hui He; Dezhong Yao; Cheng Luo
The dynamic profile of brain function has received much attention in recent years and is also a focus in the study of epilepsy. The present study aims to integrate the dynamics of temporal and spatial characteristics to provide comprehensive and novel understanding of epileptic dynamics. Resting state fMRI data were collected from eighty-three patients with idiopathic generalized epilepsy (IGE) and
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Roughness-Length-Based Characteristic Analysis of Intracranial EEG and Epileptic Seizure Prediction Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-11-16 Yanli Zhang; Rendi Yang; Weidong Zhou
To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those
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An Efficient Approach to Define the Input Stimuli to Suppress Epileptic Seizures Described by the Epileptor Model. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 João Angelo Ferres Brogin,Jean Faber,Douglas Domingues Bueno
Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like
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Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-10-09 Antonino Naro; Maria Grazia Maggio; Antonino Leo; Rocco Salvatore Calabrò
The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer
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Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-19 Feng Fang,Thomas Potter,Thinh Nguyen,Yingchun Zhang
Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization
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A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 Pankaj Mishra,Claudio Piciarelli,Gian Luca Foresti
Image anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal
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A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington's Disease. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-24 Álvaro González-Redondo,Francisco Naveros,Eduardo Ros,Jesús A Garrido
The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington’s disease (HD) affects this brain area, it still remains unclear how HD patients achieve paradoxical improvement in sensorial discrimination tasks. This paper presents a computational model of the BG including the main nuclei and the typical firing properties of
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Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-11 Manuel Graña,Marina Aguilar-Moreno,Javier De Lope Asiain,Ibai Baglietto Araquistain,Xavier Garmendia
Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where
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A Methodology to Differentiate Parkinson's Disease and Aging Speech Based on Glottal Flow Acoustic Analysis. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-03 Andrés Gómez-Rodellar,Daniel Palacios-Alonso,José M Ferrández Vicente,Jiri Mekyska,Agustín Álvarez-Marquina,Pedro Gómez-Vilda
Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral
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A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 Hiroaki Hashimoto,Seiji Kameda,Hitoshi Maezawa,Satoru Oshino,Naoki Tani,Hui Ming Khoo,Takufumi Yanagisawa,Toshiki Yoshimine,Haruhiko Kishima,Masayuki Hirata
To realize a brain–machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis
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An Adaptive Optimization Spiking Neural P System for Binary Problems. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 Ming Zhu,Qiang Yang,Jianping Dong,Gexiang Zhang,Xiantai Gou,Haina Rong,Prithwineel Paul,Ferrante Neri
Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities
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A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 Gexiang Zhang,Haina Rong,Prithwineel Paul,Yangyang He,Ferrante Neri,Mario J Pérez-Jiménez
Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is
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Quaternion Spiking and Quaternion Quantum Neural Networks: Theory and Applications. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-16 Eduardo Bayro-Corrochano,Samuel Solis-Gamboa,Guillermo Altamirano-Escobedo,Luis Lechuga-Gutierres,Jorge Lisarraga-Rodriguez
Biological evidence shows that there are neural networks specialized for recognition of signals and patterns acting as associative memories. The spiking neural networks are another kind which receive input from a broad range of other brain areas to produce output that selects particular cognitive or motor actions to perform. An important contribution of this work is to consider the geometric processing
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Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-12 Diego Castillo-Barnes,Francisco J Martinez-Murcia,Andres Ortiz,Diego Salas-Gonzalez,Javier RamÍrez,Juan M Górriz
Finding new biomarkers to model Parkinson’s Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics
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Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-21 Antonio Lozano,Juan Sebastián Suárez,Cristina Soto-Sánchez,Javier Garrigós,J Javier Martínez-Alvarez,J Manuel Ferrández,Eduardo Fernández
Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for
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Enhancement of Hippocampal Spatial Decoding Using a Dynamic Q-Learning Method With a Relative Reward Using Theta Phase Precession. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-12 Bo-Wei Chen,Shih-Hung Yang,Yu-Chun Lo,Ching-Fu Wang,Han-Lin Wang,Chen-Yang Hsu,Yun-Ting Kuo,Jung-Chen Chen,Sheng-Huang Lin,Han-Chi Pan,Sheng-Wei Lee,Xiao Yu,Boyi Qu,Chao-Hung Kuo,You-Yin Chen,Hsin-Yi Lai
Hippocampal place cells and interneurons in mammals have stable place fields and theta phase precession profiles that encode spatial environmental information. Hippocampal CA1 neurons can represent the animal’s location and prospective information about the goal location. Reinforcement learning (RL) algorithms such as Q-learning have been used to build the navigation models. However, the traditional
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Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-19 Pinar Ozel,Ali Karaca,Ali Olamat,Aydin Akan,Mehmet Akif Ozcoban,Oguz Tan
Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization
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Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-21 Lubin Wang,Xianbin Li,Yuyang Zhu,Bei Lin,Qijing Bo,Feng Li,Chuanyue Wang
Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined
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Response to Discussion on "Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease,". Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-12 Pattaramon Vuttipittayamongkol,Eyad Elyan
In the paper Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease, the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The methods involve identification and removal of potentially overlapped majority class instances. Extensive evaluations were carried out using 136 datasets
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Editorial: A Magnificent Journal at the Crossroads. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-09-01 Manuel Graña
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Editorial: Thirty Years of a Journal Fostering Interdisciplinary Research Excellence. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-19 Francesco Carlo Morabito
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Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-19 John Thomas,Jing Jin,Prasanth Thangavel,Elham Bagheri,Rajamanickam Yuvaraj,Justin Dauwels,Rahul Rathakrishnan,Jonathan J Halford,Sydney S Cash,Brandon Westover
Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this
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Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-19 Chen-Sen Ouyang,Rei-Cheng Yang,Rong-Ching Wu,Ching-Tai Chiang,Lung-Chang Lin
The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis
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Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-18 Jonathan Dan,Benjamin Vandendriessche,Wim Van Paesschen,Dorien Weckhuysen,Alexander Bertrand
Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally
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Spiking Neural P Systems with Extended Channel Rules. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-18 Zeqiong Lv,Tingting Bao,Nan Zhou,Hong Peng,Xiangnian Huang,Agustín Riscos-Núñez,Mario J Pérez-Jiménez
This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP–ECR systems). SNP–ECR systems are a class of distributed parallel computing models. In SNP–ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons
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Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-08-18 Bo Li,Hong Peng,Xiaohui Luo,Jun Wang,Xiaoxiao Song,Mario J Pérez-Jiménez,Agustín Riscos-Núñez
Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled
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Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-26 Mario Ortiz,Eduardo Iáñez,Jorge A Gaxiola-Tirado,David Gutiérrez,José M Azorín
The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on electroencephalography is simulated in offline analysis
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How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-03 Daniele Caligiore,Pierandrea Mirino
Several data have demonstrated that during the widely used experimental paradigm for studying associative learning, trace eye blinking conditioning (TEBC), there is a strong interaction between cerebellum and medial prefrontal cortex (mPFC). Despite this evidence, the neural mechanisms underlying this interaction are still not clear. Here, we propose a neurophysiologically plausible computational model
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Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-17 Pattaramon Vuttipittayamongkol,Eyad Elyan
Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced datasets focus mainly on the class distribution problem and aim at providing more balanced datasets by means of resampling. However, existing literature
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Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-14 A Apicella,F Isgrò,R Prevete,G Tamburrini
Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs
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Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-26 Foued Saâdaoui,Othman Ben Messaoud
Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction
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EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-05-28 Francisco J Martinez-Murcia,Andres Ortiz,Juan Manuel Gorriz,Javier Ramirez,Pedro Javier Lopez-Abarejo,Miguel Lopez-Zamora,Juan Luis Luque
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5–1Hz), syllabic (4–8Hz) or the phoneme (12–40Hz)
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Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-04 David López-García,Alberto Sobrado,José M G Peñalver,Juan Manuel Górriz,María Ruz
A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been
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Behavioral Activity Recognition Based on Gaze Ethograms. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-09 Javier De Lope,Manuel Graña
Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user’s behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling
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Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-05 Roberto Sánchez-Reolid,Arturo Martínez-Rodrigo,María T López,Antonio Fernández-Caballero
Early detection of stress condition is beneficial to prevent long-term mental illness like depression and anxiety. This paper introduces an accurate identification of stress/calm condition from electrodermal activity (EDA) signals. The acquisition of EDA signals from a commercial wearable as well as their storage and processing are presented. Several time-domain, frequency-domain and morphological
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Discussion on Vuttipittayamongkol, P. and Elyan, E., Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-23 Alberto Fernández
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Spiking Neural P Systems with Delay on Synapses. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-23 Xiaoxiao Song,Luis Valencia-Cabrera,Hong Peng,Jun Wang,Mario J Pérez-Jiménez
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapses, SN P systems with delay on synapses (SNP-DS systems) are proposed in this work. Unlike the traditional SN P systems, where all the postsynaptic neurons
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Announcement: The 2020 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-14
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IJNS: 30 Years of Breakthrough Multidisciplinarity, Rigor, and Excellence in the Knowledge Limits. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-07-03 Jose Manuel Ferrandez
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Introduction. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-08 José Manuel Ferrández,Diego Andina,Juan Manuel Górriz
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Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-04 Andrés Ortiz,Francisco J Martinez-Murcia,Juan L Luque,Almudena Giménez,Roberto Morales-Ortega,Julio Ortega
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent
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Single-Trial EEG Responses Classified Using Latency Features. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-02 Irzam Hardiansyah,Valentina Pergher,Marc M Van Hulle
Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-based features instead of latency-based features. In this study, we investigated changes in EEG response signatures of nine healthy older subjects when performing
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Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control. Int. J. Neural Syst. (IF 5.604) Pub Date : 2020-06-01 Filip Stojic,Tom Chau
Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously
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