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International Journal of Neural Systems

Piotr Duda, Maciej Jaworski, Leszek Rutkowski
One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches...
October 12, 2017: International Journal of Neural Systems
Camillo Porcaro, Carlo Cottone, Andrea Cancelli, Carlo Salustri, Franca Tecchio
High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i...
September 11, 2017: International Journal of Neural Systems
Xin Li, Yanqin Bai, Yaxin Peng, Shaoyi Du, Shihui Ying
Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group...
September 11, 2017: International Journal of Neural Systems
Rong Liu, Yongxuan Wang, Geoffrey I Newman, Nitish V Thakor, Sarah Ying
To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier...
September 7, 2017: International Journal of Neural Systems
Eduardo Sánchez, Rubén Ferreiroa, Adrián Arias, Luis M Martínez
The center-surround organization of the receptive fields (RFs) of retinal ganglion cells highlights the presence of local contrast in visual stimuli. As RF of thalamic relay cells follow the same basic functional organization, it is often assumed that they contribute very little to alter the retinal output. However, in many species, thalamic relay cells largely outnumber their retinal inputs, which diverge to contact simultaneously several units at thalamic level. This gain in cell population as well as retinothalamic convergence opens the door to question how information about contrast is transformed at the thalamic stage...
September 7, 2017: International Journal of Neural Systems
Felix Weissenberger, Florian Meier, Johannes Lengler, Hafsteinn Einarsson, Angelika Steger
Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains...
September 7, 2017: International Journal of Neural Systems
(no author information available yet)
No abstract text is available yet for this article.
December 2017: International Journal of Neural Systems
Dimitris Kugiumtzis, Christos Koutlis, Alkiviadis Tsimpiris, Vasilios K Kimiskidis
OBJECTIVE: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. METHODS: EEG recordings from two GGE patients were analyzed...
November 2017: International Journal of Neural Systems
(no author information available yet)
No abstract text is available yet for this article.
September 2017: International Journal of Neural Systems
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
Vasilios K Kimiskidis, Philippe Ryvlin, Steven Schachter
No abstract text is available yet for this article.
August 18, 2017: International Journal of Neural Systems
Linqiang Pan, Gheorghe Păun, Gexiang Zhang, Ferrante Neri
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron...
August 16, 2017: International Journal of Neural Systems
David López-Sanz, Pilar Garcés, Blanca Álvarez, María Luisa Delgado-Losada, Ramón López-Higes, Fernando Maestú
INTRODUCTION: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer's Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. METHODS: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups...
August 16, 2017: International Journal of Neural Systems
Shasha Yuan, Weidong Zhou, Liyan Chen
Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance...
August 16, 2017: International Journal of Neural Systems
Yong Jiao, Yu Zhang, Yu Wang, Bei Wang, Jing Jin, Xingyu Wang
Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy...
August 13, 2017: International Journal of Neural Systems
Farhan Dawood, Chu Kiong Loo
Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration...
August 6, 2017: International Journal of Neural Systems
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
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
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
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
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