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Brain Informatics

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https://read.qxmd.com/read/30721365/a-machine-learning-approach-to-predict-perceptual-decisions-an-insight-into-face-pareidolia
#1
Kasturi Barik, Syed Naser Daimi, Rhiannon Jones, Joydeep Bhattacharya, Goutam Saha
The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial...
February 5, 2019: Brain Informatics
https://read.qxmd.com/read/30706237/improved-shuffled-frog-leaping-algorithm-on-system-reliability-analysis
#2
Yancang Li, Zhen Yan
With the increase in system complexity, the intelligent heuristic optimization methods have received more and more attention on system reliability analysis. However, the objective functions and constraint conditions of system reliability are nonlinear. Thereby, a hybrid optimization method was proposed, based on the shuffled frog leaping algorithm and bacterial foraging algorithm, to solve the problem of system reliability and redundancy allocation. First, random grouping strategy was added to maintain the diversity of the population...
January 31, 2019: Brain Informatics
https://read.qxmd.com/read/30515600/waas-architecture-driven-depressive-mood-status-quantitative-analysis-based-on-forehead-eeg-and-self-rating-tool
#3
Zhijiang Wan, Hao Zhang, Jianhui Chen, Haiyan Zhou, Jie Yang, Ning Zhong
BACKGROUND: Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log...
December 5, 2018: Brain Informatics
https://read.qxmd.com/read/30499008/mental-state-and-emotion-detection-from-musically-stimulated-eeg
#4
REVIEW
Avinash L Tandle, Manjusha S Joshi, Ambrish S Dharmadhikari, Suyog V Jaiswal
This literature survey attempts to clarify different approaches considered to study the impact of the musical stimulus on the human brain using EEG Modality. Glancing at the field through various aspects of such studies specifically an experimental protocol, the EEG machine, number of channels investigated, feature extracted, categories of emotions, the brain area, the brainwaves, statistical tests, machine learning algorithms used for classification and validation of the developed model. This article comments on how these different approaches have particular weaknesses and strengths...
November 29, 2018: Brain Informatics
https://read.qxmd.com/read/30390165/a-structural-equation-model-for-imaging-genetics-using-spatial-transcriptomics
#5
Sjoerd M H Huisman, Ahmed Mahfouz, Nematollah K Batmanghelich, Boudewijn P F Lelieveldt, Marcel J T Reinders
Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction...
November 2, 2018: Brain Informatics
https://read.qxmd.com/read/30370454/side-channel-attacks-against-the-human-brain-the-pin-code-case-study-extended-version
#6
Joseph Lange, Clément Massart, André Mouraux, François-Xavier Standaert
We revisit the side-channel attacks with brain-computer interfaces (BCIs) first put forward by Martinovic et al. at the USENIX 2012 Security Symposium. For this purpose, we propose a comprehensive investigation of concrete adversaries trying to extract a PIN code from electroencephalogram signals. Overall, our results confirm the possibility of partial PIN recovery with high probability of success in a more quantified manner and at the same time put forward the challenges of full/systematic PIN recovery. They also highlight that the attack complexities can significantly vary in function of the adversarial capabilities (e...
October 29, 2018: Brain Informatics
https://read.qxmd.com/read/30175391/automated-epileptic-seizures-detection-using-multi-features-and-multilayer-perceptron-neural-network
#7
N Sriraam, S Raghu, Kadeeja Tamanna, Leena Narayan, Mehraj Khanum, A S Hegde, Anjani Bhushan Kumar
Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier...
September 3, 2018: Brain Informatics
https://read.qxmd.com/read/30159647/correction-to-two-step-verification-of-brain-tumor-segmentation-using-watershed-matching-algorithm
#8
S M Kamrul Hasan, Mohiuddin Ahmad
In the original publication of this article [1], the spelling of second author was incorrect.
August 29, 2018: Brain Informatics
https://read.qxmd.com/read/30123924/the-effects-of-emotional-states-and-traits-on-time-perception
#9
Katie A Lehockey, Andrea R Winters, Alexandra J Nicoletta, Taylor E Zurlinden, Daniel E Everhart
BACKGROUND: Models of time perception share an element of scalar expectancy theory known as the internal clock, containing specific mechanisms by which the brain is able to experience time passing and function effectively. A debate exists about whether to treat factors that influence these internal clock mechanisms (e.g., emotion, personality, executive functions, and related neurophysiological components) as arousal- or attentional-based factors. PURPOSE: This study investigated behavioral and neurophysiological responses to an affective time perception Go/NoGo task, taking into account the behavioral inhibition (BIS) and behavioral activation systems (BASs), which are components of reinforcement sensitivity theory...
August 20, 2018: Brain Informatics
https://read.qxmd.com/read/30105425/two-step-verification-of-brain-tumor-segmentation-using-watershed-matching-algorithm
#10
REVIEW
S M Kamrul Hasan, Mohiudding Ahmad
Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Past researchers used biopsy to detect the tumor tissue from the other soft tissues in the brain which is time-consuming and may have errors. We outlined a two-stage verification-based tumor segmentation that makes the detection more accurate...
August 14, 2018: Brain Informatics
https://read.qxmd.com/read/30022317/thought-chart-tracking-the-thought-with-manifold-learning-during-emotion-regulation
#11
Mengqi Xing, Johnson GadElkarim, Olusola Ajilore, Ouri Wolfson, Angus Forbes, K Luan Phan, Heide Klumpp, Alex Leow
The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants' EEG recordings during resting state and an emotion regulation task...
July 19, 2018: Brain Informatics
https://read.qxmd.com/read/29987692/various-epileptic-seizure-detection-techniques-using-biomedical-signals-a-review
#12
REVIEW
Yash Paul
Epilepsy is a chronic chaos of the central nervous system that influences individual's daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain's electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs...
July 10, 2018: Brain Informatics
https://read.qxmd.com/read/29968092/a-review-and-outlook-on-visual-analytics-for-uncertainties-in-functional-magnetic-resonance-imaging
#13
REVIEW
Michael de Ridder, Karsten Klein, Jinman Kim
Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps...
July 3, 2018: Brain Informatics
https://read.qxmd.com/read/29904812/review-of-eeg-based-pattern-classification-frameworks-for-dyslexia
#14
Harshani Perera, Mohd Fairuz Shiratuddin, Kok Wai Wong
Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research...
June 15, 2018: Brain Informatics
https://read.qxmd.com/read/29876679/deepneuron-an-open-deep-learning-toolbox-for-neuron-tracing
#15
Zhi Zhou, Hsien-Chi Kuo, Hanchuan Peng, Fuhui Long
Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks...
June 6, 2018: Brain Informatics
https://read.qxmd.com/read/29881932/a-3d-stereotactic-atlas-of-the-adult-human-skull-base
#16
Wieslaw L Nowinski, Thant S L Thaung
BACKGROUND: The skull base region is anatomically complex and poses surgical challenges. Although many textbooks describe this region illustrated well with drawings, scans and photographs, a complete, 3D, electronic, interactive, realistic, fully segmented and labeled, and stereotactic atlas of the skull base has not yet been built. Our goal is to create a 3D electronic atlas of the adult human skull base along with interactive tools for structure manipulation, exploration, and quantification...
May 31, 2018: Brain Informatics
https://read.qxmd.com/read/29881892/brain-mri-analysis-for-alzheimer-s-disease-diagnosis-using-an-ensemble-system-of-deep-convolutional-neural-networks
#17
Jyoti Islam, Yanqing Zhang
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people...
May 31, 2018: Brain Informatics
https://read.qxmd.com/read/29322469/removal-of-muscular-artifacts-in-eeg-signals-a-comparison-of-linear-decomposition-methods
#18
Laura Frølich, Irene Dowding
The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods' ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements...
March 2018: Brain Informatics
https://read.qxmd.com/read/29313301/identification-and-classification-of-brain-tumor-mri-images-with-feature-extraction-using-dwt-and-probabilistic-neural-network
#19
N Varuna Shree, T N R Kumar
The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain...
March 2018: Brain Informatics
https://read.qxmd.com/read/29224063/an-efficient-scheme-for-mental-task-classification-utilizing-reflection-coefficients-obtained-from-autocorrelation-function-of-eeg-signal
#20
M M Rahman, M A Chowdhury, S A Fattah
Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI...
March 2018: Brain Informatics
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