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A global framework for a systemic view of brain modeling Brain Inf. Pub Date : 2021-02-16 Frederic Alexandre
The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and
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Detecting depression using an ensemble classifier based on Quality of Life scales Brain Inf. Pub Date : 2021-02-15 Xiaohui Tao; Oliver Chi; Patrick J. Delaney; Lin Li; Jiajin Huang
Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research,
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Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals Brain Inf. Pub Date : 2021-02-12 Athar A. Ein Shoka; Monagi H. Alkinani; A. S. El-Sherbeny; Ayman El-Sayed; Mohamed M. Dessouky
Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper
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A dynamic causal model on self-regulation of aversive emotion Brain Inf. Pub Date : 2020-12-09 Yang Yang; Xiaofei Zhang; Yue Peng; Jie Bai; Xiuya Lei
Cognitive regulation of emotion has been proven to be effective to take control the emotional responses. Some cognitive models have also been proposed to explain the neural mechanism that underlies this process. However, some characteristics of the models are still unclear, such as whether the cognitive regulation will be spontaneously employed by participants implicitly. The present study recruited
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Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment Brain Inf. Pub Date : 2020-11-26 D. Rangaprakash; Toluwanimi Odemuyiwa; D. Narayana Dutt; Gopikrishna Deshpande
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues,
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An event based topic learning pipeline for neuroimaging literature mining Brain Inf. Pub Date : 2020-11-23 Lihong Chen; Jianzhuo Yan; Jianhui Chen; Ying Sheng; Zhe Xu; Mufti Mahmud
Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning
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Gamma entrainment frequency affects mood, memory and cognition: an exploratory pilot study Brain Inf. Pub Date : 2020-11-23 Ryan L. S. Sharpe; Mufti Mahmud; M. Shamim Kaiser; Jianhui Chen
Here we provide evidence with an exploratory pilot study that through the use of a Gamma 40 Hz entrainment frequency, mood, memory and cognition can be improved with respect to a 9-participant cohort. Participants constituted towards three binaural entrainment frequency groups: the 40 Hz, 25 Hz and 100 Hz. Participants attended a total of eight entrainment frequency sessions twice over the duration
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Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science Brain Inf. Pub Date : 2020-11-16 Rui Li; Junsong Zhang
The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine
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Neuropsychopharmacological effects of midazolam on the human brain Brain Inf. Pub Date : 2020-11-10 Junkai Wang; Pei Sun; Peipeng Liang
As a commonly used anesthetic agent, midazolam has the properties of water-soluble, rapid onset, and short duration of action. With the rapid development in the field of neuroimaging, numerous studies have investigated how midazolam acts on the human brain to induce the alteration of consciousness. However, the neural bases of midazolam-induced sedation or anesthesia remain beginning to be understood
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Retrieving similar substructures on 3D neuron reconstructions Brain Inf. Pub Date : 2020-11-04 Jian Yang; Yishan He; Xuefeng Liu
Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar
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Localization of epileptic seizure focus by computerized analysis of fMRI recordings Brain Inf. Pub Date : 2020-10-31 Rasoul Hekmati; Robert Azencott; Wei Zhang; Zili D. Chu; Michael J. Paldino
By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of
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Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis Brain Inf. Pub Date : 2020-10-22 Md. Sakibur Rahman Sajal; Md. Tanvir Ehsan; Ravi Vaidyanathan; Shouyan Wang; Tipu Aziz; Khondaker Abdullah Al Mamun
With the growing number of the aged population, the number of Parkinson’s disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients’ symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work
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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia Brain Inf. Pub Date : 2020-10-09 Manan Binth Taj Noor; Nusrat Zerin Zenia; M Shamim Kaiser; Shamim Al Mamun; Mufti Mahmud
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease
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Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. Pub Date : 2020-09-21 Ferdousi Sabera Rawnaque,Khandoker Mahmudur Rahman,Syed Ferhat Anwar,Ravi Vaidyanathan,Tom Chau,Farhana Sarker,Khondaker Abdullah Al Mamun
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this
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Two-stepped majority voting for efficient EEG-based emotion classification. Brain Inf. Pub Date : 2020-09-17 Aras M Ismael,Ömer F Alçin,Karmand Hussein Abdalla,Abdulkadir Şengür
In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach
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CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification. Brain Inf. Pub Date : 2020-09-03 D F Collazos-Huertas,A M Álvarez-Meza,C D Acosta-Medina,G A Castaño-Duque,G Castellanos-Dominguez
Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns
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Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation. Brain Inf. Pub Date : 2020-06-16 Md Asadur Rahman,Farzana Khanam,Mohiuddin Ahmad,Mohammad Shorif Uddin
This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection
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EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features. Brain Inf. Pub Date : 2020-05-29 Negar Ahmadi,Yulong Pei,Evelien Carrette,Albert P Aldenkamp,Mykola Pechenizkiy
Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined
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A review of epileptic seizure detection using machine learning classifiers. Brain Inf. Pub Date : 2020-05-25 Mohammad Khubeb Siddiqui,Ruben Morales-Menendez,Xiaodi Huang,Nasir Hussain
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear
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Epileptic spikes detector in pediatric EEG based on matched filters and neural networks. Brain Inf. Pub Date : 2020-05-24 Maritza Mera-Gaona,Diego M López,Rubiel Vargas-Canas,María Miño
The electroencephalogram (EEG) is a tool for diagnosing epilepsy; by analyzing it, neurologists can identify alterations in brain activity associated with epilepsy. However, this task is not always easy to perform because of the duration of the EEG or the subjectivity of the specialist in detecting alterations. To propose the use of an epileptic spike detector based on a matched filter and a neural
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GAN-based synthetic brain PET image generation Brain Inf. Pub Date : 2020-03-30 Jyoti Islam; Yanqing Zhang
In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach
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An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology Brain Inf. Pub Date : 2020-03-26 Kayvan Bijari; Masood A. Akram; Giorgio A. Ascoli
Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared
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Improved fruit fly algorithm on structural optimization. Brain Inf. Pub Date : 2020-02-16 Yancang Li,Muxuan Han
To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall
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Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks. Brain Inf. Pub Date : 2019-12-02 Sinan Zhao,D Rangaprakash,Peipeng Liang,Gopikrishna Deshpande
It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness
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Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage. Brain Inf. Pub Date : 2017-01-12 Priya Aggarwal,Parth Shrivastava,Tanay Kabra,Anubha Gupta
This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the
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An adaptive annotation approach for biomedical entity and relation recognition. Brain Inf. Pub Date : 2016-10-18 Seid Muhie Yimam,Chris Biemann,Ljiljana Majnaric,Šefket Šabanović,Andreas Holzinger
In this article, we demonstrate the impact of interactive machine learning: we develop biomedical entity recognition dataset using a human-into-the-loop approach. In contrary to classical machine learning, human-in-the-loop approaches do not operate on predefined training or test sets, but assume that human input regarding system improvement is supplied iteratively. Here, during annotation, a machine
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Identifying HIV-induced subgraph patterns in brain networks with side information. Brain Inf. Pub Date : 2016-10-18 Bokai Cao,Xiangnan Kong,Jingyuan Zhang,Philip S Yu,Ann B Ragin
Investigating brain connectivity networks for neurological disorder identification has attracted great interest in recent years, most of which focus on the graph representation alone. However, in addition to brain networks derived from the neuroimaging data, hundreds of clinical, immunologic, serologic, and cognitive measures may also be documented for each subject. These measures compose multiple
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Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inf. Pub Date : 2016-10-18 Sidong Liu,Weidong Cai,Siqi Liu,Fan Zhang,Michael Fulham,Dagan Feng,Sonia Pujol,Ron Kikinis
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function
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Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties. Brain Inf. Pub Date : 2016-10-18 D J Hamilton,D W Wheeler,C M White,C L Rees,A O Komendantov,M Bergamino,G A Ascoli
Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. Hippocampome.org is a web-accessible neuroinformatics resource that organizes existing data about essential properties of all known neuron types in the rodent hippocampal formation
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Familiarity effects in EEG-based emotion recognition. Brain Inf. Pub Date : 2016-10-18 Nattapong Thammasan,Koichi Moriyama,Ken-Ichi Fukui,Masayuki Numao
Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity
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It's not what you expect: feedback negativity is independent of reward expectation and affective responsivity in a non-probabilistic task. Brain Inf. Pub Date : 2016-10-18 Jonathan M Highsmith,Karl L Wuensch,Tuan Tran,Alexandra J Stephenson,D Erik Everhart
ERP studies commonly utilize gambling-based reinforcement tasks to elicit feedback negativity (FN) responses. This study used a pattern learning task in order to limit gambling-related fallacious reasoning and possible affective responses to gambling, while investigating relationships between the FN components between high and low reward expectation conditions. Eighteen undergraduates completed measures
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Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop. Brain Inf. Pub Date : 2016-10-18 Michael Hund,Dominic Böhm,Werner Sturm,Michael Sedlmair,Tobias Schreck,Torsten Ullrich,Daniel A Keim,Ljiljana Majnaric,Andreas Holzinger
Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along
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Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inf. Pub Date : 2016-10-18 Sarni Suhaila Rahim,Vasile Palade,James Shuttleworth,Chrisina Jayne
Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images
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The structure function as new integral measure of spatial and temporal properties of multichannel EEG. Brain Inf. Pub Date : 2016-10-18 Mikhail Trifonov
The first-order temporal structure functions (SFs), i.e., the first-order statistical moment of absolute increments of scaled multichannel resting state EEG signals in healthy children and teenagers over a wide range of temporal separation (time lags) are computed. Our research shows that the sill level (asymptote) of the SF is mainly defined by a determinant of EEG correlation matrix reflecting the
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Frontal lobe regulation of blood glucose levels: support for the limited capacity model in hostile violence-prone men. Brain Inf. Pub Date : 2016-10-18 Robert P Walters,Patti Kelly Harrison,Ransom W Campbell,David W Harrison
Hostile men have reliably displayed an exaggerated sympathetic stress response across multiple experimental settings, with cardiovascular reactivity for blood pressure and heart rate concurrent with lateralized right frontal lobe stress (Trajanoski et al., in Diabetes Care 19(12):1412-1415, 1996; see Heilman et al., in J Neurol Neurosurg Psychiatry 38(1):69-72, 1975). The current experiment examined
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Reconstructing the brain: from image stacks to neuron synthesis. Brain Inf. Pub Date : 2016-10-18 Julian C Shillcock,Michael Hawrylycz,Sean Hill,Hanchuan Peng
Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this
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Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Inf. Pub Date : 2016-10-18 Evangelia I Zacharaki,Iosif Mporas,Kyriakos Garganis,Vasileios Megalooikonomou
Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform
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Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. Pub Date : 2016-10-18 Andreas Holzinger
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems
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Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inf. Pub Date : 2016-10-18 Hadi Ratham Al Ghayab,Yan Li,Shahab Abdulla,Mohammed Diykh,Xiangkui Wan
Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique
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Wavelet-based study of valence-arousal model of emotions on EEG signals with LabVIEW. Brain Inf. Pub Date : 2016-10-18 Seda Guzel Aydin,Turgay Kaya,Hasan Guler
This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed
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Epileptic seizure detection from EEG signals using logistic model trees. Brain Inf. Pub Date : 2016-10-18 Enamul Kabir,Siuly,Yanchun Zhang
Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic
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Time-frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain Inf. Pub Date : 2016-10-18 Abdulkadir Şengür,Yanhui Guo,Yaman Akbulut
Detection of epileptic seizure in electroencephalogram (EEG) signals is a challenging task and requires highly skilled neurophysiologists. Therefore, computer-aided detection helps neurophysiologist in interpreting the EEG. In this paper, texture representation of the time-frequency (t-f) image-based epileptic seizure detection is proposed. More specifically, we propose texture descriptor-based features
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Brain response pattern identification of fMRI data using a particle swarm optimization-based approach. Brain Inf. Pub Date : 2016-10-18 Xinpei Ma,Chun-An Chou,Hiroki Sayama,Wanpracha Art Chaovalitwongse
Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be
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Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study. Brain Inf. Pub Date : 2016-10-18 Miaolin Fan,Chun-An Chou
Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate
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An integrated feature ranking and selection framework for ADHD characterization. Brain Inf. Pub Date : 2016-10-18 Cao Xiao,Jesse Bledsoe,Shouyi Wang,Wanpracha Art Chaovalitwongse,Sonya Mehta,Margaret Semrud-Clikeman,Thomas Grabowski
Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor's experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD
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Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Inf. Pub Date : 2016-10-18 Dominic Girardi,Josef Küng,Raimund Kleiser,Michael Sonnberger,Doris Csillag,Johannes Trenkler,Andreas Holzinger
Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose
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HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI). Brain Inf. Pub Date : 2016-10-18 Milad Makkie,Shijie Zhao,Xi Jiang,Jinglei Lv,Yu Zhao,Bao Ge,Xiang Li,Junwei Han,Tianming Liu
Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI
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Quantitative electroencephalographic and neuropsychological investigation of an alternative measure of frontal lobe executive functions: the Figure Trail Making Test. Brain Inf. Pub Date : 2016-10-18 Paul S Foster,Valeria Drago,Brad J Ferguson,Patti Kelly Harrison,David W Harrison
The most frequently used measures of executive functioning are either sensitive to left frontal lobe functioning or bilateral frontal functioning. Relatively little is known about right frontal lobe contributions to executive functioning given the paucity of measures sensitive to right frontal functioning. The present investigation reports the development and initial validation of a new measure designed
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The intrinsic geometry of the human brain connectome. Brain Inf. Pub Date : 2016-10-18 Allen Q Ye,Olusola A Ajilore,Giorgio Conte,Johnson GadElkarim,Galen Thomas-Ramos,Liang Zhan,Shaolin Yang,Anand Kumar,Richard L Magin,Angus G Forbes,Alex D Leow
This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear
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A review of heterogeneous data mining for brain disorder identification. Brain Inf. Pub Date : 2016-10-18 Bokai Cao,Xiangnan Kong,Philip S Yu
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and
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Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inf. Pub Date : 2016-10-18 Sidong Liu,Weidong Cai,Siqi Liu,Fan Zhang,Michael Fulham,Dagan Feng,Sonia Pujol,Ron Kikinis
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal
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SmartTracing: self-learning-based Neuron reconstruction. Brain Inf. Pub Date : 2016-10-18 Hanbo Chen,Hang Xiao,Tianming Liu,Hanchuan Peng
In this work, we propose SmartTracing, an automatic tracing framework that does not require substantial human intervention. There are two major novelties in SmartTracing. First, given an input image, SmartTracing invokes a user-provided existing neuron tracing method to produce an initial neuron reconstruction, from which the likelihood of every neuron reconstruction unit is estimated. This likelihood
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Local regression transfer learning with applications to users' psychological characteristics prediction. Brain Inf. Pub Date : 2016-10-18 Zengda Guan,Ang Li,Tingshao Zhu
It is important to acquire web users' psychological characteristics. Recent studies have built computational models for predicting psychological characteristics by supervised learning. However, the generalization of built models might be limited due to the differences in distribution between the training and test dataset. To address this problem, we propose some local regression transfer learning methods
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A comparison of several computational auditory scene analysis (CASA) techniques for monaural speech segregation. Brain Inf. Pub Date : 2016-10-18 Jihen Zeremdini,Mohamed Anouar Ben Messaoud,Aicha Bouzid
Humans have the ability to easily separate a composed speech and to form perceptual representations of the constituent sources in an acoustic mixture thanks to their ears. Until recently, researchers attempt to build computer models of high-level functions of the auditory system. The problem of the composed speech segregation is still a very challenging problem for these researchers. In our case, we
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Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Brain Inf. Pub Date : 2016-08-16 Dragan Gamberger,Bernard Ženko,Alexis Mitelpunkt,Netta Shachar,Nada Lavrač
This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological
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Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Inf. Pub Date : 2016-05-13 Xiaohui Yao,Jingwen Yan,Sungeun Kim,Kwangsik Nho,Shannon L Risacher,Mark Inlow,Jason H Moore,Andrew J Saykin,Li Shen
Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation
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A tamper-proof audit and control system for the doctor in the loop. Brain Inf. Pub Date : 2016-03-19 Peter Kieseberg,Bernd Malle,Peter Frühwirt,Edgar Weippl,Andreas Holzinger
The "doctor in the loop" is a new paradigm in information-driven medicine, picturing the doctor as authority inside a loop supplying an expert system with information on actual patients, treatment results, and possible additional (side-)effects, including general information in order to enhance data-driven medical science, as well as giving back treatment advice to the doctor himself. While this approach
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The diversity rank-score function for combining human visual perception systems. Brain Inf. Pub Date : 2016-02-15 Christina Schweikert,Darius Mulia,Kilby Sanchez,D Frank Hsu
There are many situations in which a joint decision, based on the observations or decisions of multiple individuals, is desired. The challenge is determining when a combined decision is better than each of the individual systems, along with choosing the best way to perform the combination. It has been shown that the diversity between systems plays a role in the performance of their fusion. This study
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Diagonal queue medical image steganography with Rabin cryptosystem. Brain Inf. Pub Date : 2016-02-15 Mamta Jain,Saroj Kumar Lenka
The main purpose of this work is to provide a novel and efficient method to the image steganography area of research in the field of biomedical, so that the security can be given to the very precious and confidential sensitive data of the patient and at the same time with the implication of the highly reliable algorithms will explode the high security to the precious brain information from the intruders
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Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Inf. Pub Date : 2016-02-01 Ahmad Chaddad,Camel Tanougast
To isolate the brain from non-brain tissues using a fully automatic method may be affected by the presence of radio frequency non-homogeneity of MR images (MRI), regional anatomy, MR sequences, and the subjects of the study. In order to automate the brain tumor (Glioblastoma) detection, we proposed a novel approach of skull stripping for axial slices derived from MRI. Then, the brain tumor was detected
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