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International Journal of Neural Systems

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https://www.readbyqxmd.com/read/29768990/control-of-a-7-dof-robotic-arm-system-with-an-ssvep-based-bci
#1
Xiaogang Chen, Bing Zhao, Yijun Wang, Shengpu Xu, Xiaorong Gao
Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system...
April 12, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29768989/a-novel-method-of-building-functional-brain-network-using-deep-learning-algorithm-with-application-in-proficiency-detection
#2
Chengcheng Hua, Hong Wang, Hong Wang, Shaowen Lu, Chong Liu, Syed Madiha Khalid
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper...
April 11, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29768988/single-cell-recordings-to-target-the-anterior-nucleus-of-the-thalamus-in-deep-brain-stimulation-for-patients-with-refractory-epilepsy
#3
Frédéric L W V J Schaper, Yan Zhao, Marcus L F Janssen, G Louis Wagner, Albert J Colon, Danny M W Hilkman, Erik Gommer, Mariëlle C G Vlooswijk, Govert Hoogland, Linda Ackermans, Lo J Bour, Richard J A Van Wezel, Paul Boon, Yasin Temel, Tjitske Heida, Vivianne H J M Van Kranen-Mastenbroek, Rob P W Rouhl
Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is a promising treatment for patients with refractory epilepsy. However, therapy response varies and precise positioning of the DBS lead is potentially essential for maximizing therapeutic efficacy. We investigate if single-cell recordings acquired by microelectrode recordings can aid targeting of the ANT during surgery and hypothesize that the neuronal firing properties of the target region relate to clinical outcome. We prospectively included 10 refractory epilepsy patients and performed microelectrode recordings under general anesthesia to identify the change in neuronal signals when approaching and transecting the ANT...
April 2, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29768971/integrating-eeg-and-meg-signals-to-improve-motor-imagery-classification-in-brain-computer-interface
#4
Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent Hugueville, Ankit N Khambhati, Danielle S Bassett, Fabrizio De Vico Fallani
We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs...
April 2, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29759014/simplified-and-yet-turing-universal-spiking-neural-p-systems-with-communication-on-request
#5
Tingfang Wu, Florin-Daniel Bîlbîe, Andrei Păun, Linqiang Pan, Ferrante Neri
Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike...
April 2, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29747532/neonatal-seizure-detection-using-deep-convolutional-neural-networks
#6
Amir H Ansari, Perumpillichira J Cherian, Alexander Caicedo, Gunnar Naulaers, Maarten De Vos, Sabine Van Huffel
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure...
April 2, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29665725/epileptic-eeg-identification-via-lbp-operators-on-wavelet-coefficients
#7
Qi Yuan, Weidong Zhou, Fangzhou Xu, Yan Leng, Dongmei Wei
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals...
March 19, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29631503/deep-neural-architectures-for-mapping-scalp-to-intracranial-eeg
#8
Andreas Antoniades, Loukianos Spyrou, David Martin-Lopez, Antonio Valentin, Gonzalo Alarcon, Saeid Sanei, Clive Cheong Took
Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance...
March 19, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29673276/introduction
#9
Diego Andina, Kunihiko Fukushima, Javier Ropero Peláez, Duc Truong Pham
No abstract text is available yet for this article.
June 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297262/tracking-the-reorganization-of-module-structure-in-time-varying-weighted-brain-functional-connectivity-networks
#10
Christoph Schmidt, Diana Piper, Britta Pester, Andreas Mierau, Herbert Witte
Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29297261/multi-step-time-series-forecasting-with-an-ensemble-of-varied-length-mixture-models
#11
Yicun Ouyang, Hujun Yin
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29281922/development-of-a-ternary-near-infrared-spectroscopy-brain-computer-interface-online-classification-of-verbal-fluency-task-stroop-task-and-rest
#12
Larissa C Schudlo, Tom Chau
The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29022403/developmental-approach-for-behavior-learning-using-primitive-motion-skills
#13
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...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28982285/a-novel-multilayer-correlation-maximization-model-for-improving-cca-based-frequency-recognition-in-ssvep-brain-computer-interface
#14
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...
May 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29258366/dynamic-characteristics-of-a-new-three-dimensional-linear-homeomorphic-saccade-model
#15
Wei Zhou, Xiu Zhai, Alireza Ghahari, G Alex Korentis, David Kaputa, John D Enderle
A linear homeomorphic eye movement model that produces 3D saccadic eye movements consistent with anatomical and physiological evidence is introduced in this second part of a two-paper sequence. Central to the model is the implementation of a time-optimal neural control strategy involving six linear muscle models that faithfully represent the dynamic characteristics of 3D saccades. The muscle is modeled as a parallel combination of viscosity [Formula: see text] and series elasticity [Formula: see text], connected to the parallel combination of active-state tension generator [Formula: see text], viscosity element [Formula: see text], and length tension elastic element [Formula: see text]...
April 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29241397/static-characteristics-of-a-new-three-dimensional-linear-homeomorphic-saccade-model
#16
Wei Zhou, Xiu Zhai, Alireza Ghahari, G Alex Korentis, David Kaputa, John D Enderle
A linear homeomorphic saccade model that produces 3D saccadic eye movements consistent with physiological and anatomical evidence is introduced. Central to the model is the implementation of a time-optimal controller with six linear muscles and pulleys that represent the saccade oculomotor plant. Each muscle is modeled as a parallel combination of viscosity [Formula: see text] and series elasticity [Formula: see text] connected to the parallel combination of active-state tension generator [Formula: see text], viscosity element [Formula: see text], and length tension elastic element [Formula: see text]...
April 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29113518/functional-semi-blind-source-separation-identifies-primary-motor-area-without-active-motor-execution
#17
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...
April 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28768447/neural-oscillation-correlates-chemistry-decision-making
#18
Li-Yu Huang, Hsiao-Ching She, Tzyy-Ping Jung
This study explored the electroencephalography (EEG) dynamics during a chemistry-related decision-making task and further examined whether the correctness of the decision-making performance could be reflected by EEG activity. A total of 66 undergraduate students' EEG were collected while they participated in a chemistry-related decision-making task in which they had to retrieve the relevant chemistry concepts in order to make correct decisions for each task item. The results showed that it was only in the anterior cingulate cortex (ACC) cluster that distinct patterns in EEG dynamics were displayed for the correct and incorrect responses...
April 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28633550/right-fronto-temporal-eeg-can-differentiate-the-affective-responses-to-award-winning-advertisements
#19
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 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29129128/convergent-time-varying-regression-models-for-data-streams-tracking-concept-drift-by-the-recursive-parzen-based-generalized-regression-neural-networks
#20
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...
March 2018: International Journal of Neural Systems
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