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Detection of Cognitive States from fMRI Data Using Machine Learning Techniques

Vishwajeet Singh, Krishna P. Miyapuram, Raju S. Bapi
2007 International Joint Conference on Artificial Intelligence  
Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cognitive state.  ...  Discriminating features and activity based features were used to select features for the problem of identifying the instantaneous cognitive state given a single fMRI scan and correlation based features  ...  Acknowledgments The fMRI data was collected by grants from Kawato Dynamic Brain Project, Exploratory Research for Advanced Technology, Japan. We thank Drs.  ... 
dblp:conf/ijcai/SinghMB07 fatcat:44jh5sx4wzgl7e6krlnx3e2hni

Classifying instantaneous cognitive states from FMRI data

Tom M Mitchell, Rebecca Hutchinson, Marcel A Just, Radu S Niculescu, Francisco Pereira, Xuerui Wang
2003 AMIA Annual Symposium Proceedings  
We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data.  ...  We describe a machine learning approach to this problem, and report on its successful use for discriminating cognitive states such as observing a picture versus reading a sentence, and reading a word about  ...  INTRODUCTION The study of human brain function has received a tremendous boost in recent years from the advent of functional Magnetic Resonance Imaging (fMRI), a brain imaging method that dramatically  ... 
pmid:14728216 pmcid:PMC1479944 fatcat:ejak5ptfgjgmbpcxbx2lyh7si4

Real-time fMRI-based Brain Computer Interface: A Review [article]

Yang Wang, Dongrui Wu
2018 arXiv   pre-print
Among them, the functional magnetic resonance imaging (fMRI), which has high spatial resolution, acceptable temporal resolution, simple calibration, and short preparation time, has been widely used in  ...  This paper reviews the basic architecture of rtfMRI-BCI, the emerging machine learning based data analysis approaches (also known as multi-voxel pattern analysis), and the applications and recent advances  ...  the resulting method is called multi-voxel pattern analysis (MVPA).  ... 
arXiv:1808.05852v1 fatcat:loqn5vmpujgsdkfkxbpe6yfewa

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Shuo Huang, Wei Shao, Mei-Ling Wang, Dao-Qiang Zhang
2021 International Journal of Automation and Computing  
In this paper, we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects, i.e., brain image functional alignment  ...  Recent progress in neuroimaging techniques have validated that it is possible to decode a person's thoughts, memories, and emotions via functional magnetic resonance imaging (i.e., fMRI) since it can measure  ...  Acknowledgements This work was supported by National Natural Science  ... 
doi:10.1007/s11633-020-1263-y fatcat:kwls2cvw4zgd5dti5d54uy6pgi

Hierarchical Feature Extraction for Early Alzheimer's Disease Diagnosis

Lulu Yue, Xiaoliang Gong, Jie Li, Hongfei Ji, Maozhen Li, Asoke K. Nandi
2019 IEEE Access  
Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). In this paper, we propose a novel voxel-based hierarchical feature extraction (VHFE) method for the early AD diagnosis.  ...  To split the uninformative data, we select the informative voxels in each ROI with a baseline of their values and arrange them into a vector.  ...  As a safe, rapid accurate clinical diagnosis method without any harm to human body, Magnetic resonance imaging (MRI) is widely used in clinical diagnosis.  ... 
doi:10.1109/access.2019.2926288 fatcat:6avozb3msjhtxpsprhn3ov7zay

Fmri Sonification & Brain Activity Prediction [article]

Imanol Gómez Rubio, Rafael Ramírez
2013 Zenodo  
The study of human brain functions has dramatically increased greatly due to the advent of functional Magnetic Resonance Imaging (fMRI), arguably the best technique for observing human brain activity that  ...  The goal of this tool is to allow the auditory identification of cognitive states produced by different stimuli. The system consists of a feature selection component and a sonification engine.  ...  We would like to thank Jessica Grahn for providing the fMRI data used in this paper.  ... 
doi:10.5281/zenodo.1161174 fatcat:xibit3dldrajxnfevo5mkgijke

Mesh Learning for Classifying Cognitive Processes [article]

Mete Ozay, Ilke Öztekin, Uygar Öztekin, Fatos T. Yarman Vural
2015 arXiv   pre-print
The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously  ...  MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity.  ...  An important step for MVPA is to extract relevant features acquired from neuroimaging methods, such as functional magnetic resonance imaging (fMRI).  ... 
arXiv:1205.2382v3 fatcat:iqwyj3lk6fg6fhnk6qjft7quze

Learning to Decode Cognitive States from Brain Images

Tom M. Mitchell, Rebecca Hutchinson, Radu S. Niculescu, Francisco Pereira, Xuerui Wang, Marcel Just, Sharlene Newman
2004 Machine Learning  
Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain.  ...  We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval.  ...  Francisco Pereira was funded by the Center for Neural Basis of Cognition, a PRAXIS XXI scholarship from Fundação para a Ciência e Tecnologia, Portugal (III Quadro Comunitário de Apoio, comparticipado pelo  ... 
doi:10.1023/b:mach.0000035475.85309.1b fatcat:kanilaqoa5bobgge6yg6zbphmu

Simulation of Sparse Model for Fmri Signal with Brain Activation During Hunger Regulation Process

2020 International Journal of Engineering and Advanced Technology  
Functional Magnetic Resonance Imaging (fMRI), a non-invasive technique, is used for the recognition of different Cerebral Blood Flow (CBF) and Blood Oxygenated level dependent (BOLD) measures which result  ...  A sparse model provides a well define results for task based localized activity. It can be applied on a single image as well as an fMRI dataset.  ...  Multi-voxel pattern Analysis (MVPA) examines the mapping between activities and cognitive states of brain.  ... 
doi:10.35940/ijeat.c6420.029320 fatcat:yurrdpda2retzoye4jb5ng3ecq

Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging

Zhiying Long, Yubao Wang, Xuanping Liu, Li Yao, Yong Fan
2019 PLoS ONE  
Multivariate analysis methods have been widely applied to decode brain states from functional magnetic resonance imaging (fMRI) data.  ...  Among various multivariate analysis methods, partial least squares regression (PLSR) is often used to select relevant features for decoding brain states.  ...  Multi-voxel pattern analysis (MVPA) using machine learning models has been widely applied to functional magnetic resonance imaging (fMRI) datasets to address this question [1] .  ... 
doi:10.1371/journal.pone.0214937 pmid:30970029 pmcid:PMC6457628 fatcat:s2zhuoz2jvelvj2xwwxwhzgt4q

PATTERNS OF SOCIAL INTERACTION IN BRAIN DISEASE

EDWIN A. WEINSTEIN, ROBERT L. KAHN
1956 American Journal of Psychiatry  
Functional magnetic resonance imaging (FMRI) patterns provides the prospective to study brain function in a non-invasive way.  ...  Feature subset selection (FSS) is a technique to preprocess the data before performing any data mining tasks, e.g., classification and clustering.  ...  Functional magnetic resonance imaging (FMRI) opens up the opportunity to study human brain function in a noninvasive way.  ... 
doi:10.1176/ajp.113.2.138 pmid:13340014 fatcat:s7xwe3vt5bcblon3znj7nw6yxu

Decoding brain states using functional magnetic resonance imaging

Dongha Lee, Bumhee Park, Changwon Jang, Hae-Jeong Park
2011 Biomedical Engineering Letters  
It is generally conducted using multi-voxel pattern analysis based on neuroscientific evidence that brain functions are mediated by distributed activation patterns.  ...  Most leading research in basic and clinical neuroscience has been carried out by functional magnetic resonance imaging (fMRI), which detects the blood oxygenation level dependent signals associated with  ...  INTRODUCTION The advancement of functional magnetic resonance imaging (fMRI) has greatly influenced modern brain research.  ... 
doi:10.1007/s13534-011-0021-z fatcat:kxp7bsgscfc3hkn73nbgt6sfea

Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model

Maryam Gholami Doborjeh, Elisa Capecci, Nikola Kasabov
2014 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)  
The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity.  ...  Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences).  ...  This data is based on magnetic resonance, which can be affected by blood flow changing against neuronal activities.  ... 
doi:10.1109/eals.2014.7009506 dblp:conf/eals/DoborjehCK14 fatcat:lzizsc7wpzflfcgy6fie64moia

Resting state functional magnetic resonance imaging reveals distinct brain activity in heavy cannabis users – a multi-voxel pattern analysis

H Cheng, PD Skosnik, BJ Pruce, MS Brumbaugh, JM Vollmer, DJ Fridberg, BF O'Donnell, WP Hetrick, SD Newman
2014 Journal of Psychopharmacology  
This study aims to identify the changes from resting state functional magnetic resonance imaging scans.  ...  Based on the functional connectivity of these clusters, a high overall accuracy rate of 84-88% in classification accuracy was achieved.  ...  Yi Zhang for discussion of the methods.  ... 
doi:10.1177/0269881114550354 pmid:25237118 pmcid:PMC4427512 fatcat:ylk56ujsyzbkxfrq62643vzyxq

Classification of ADHD individuals and neurotypicals using reliable RELIEF: A resting-state study

B. Miao, L. L. Zhang, J. L. Guan, Q. F. Meng, Y. L. Zhang.
2019 IEEE Access  
INDEX TERMS Medical diagnosis, image classification, feature extraction, magnetic resonance imaging.  ...  Feature selection on functional magnetic resonance imaging (fMRI) data combined with fALFF can be well used to study the pathology of attention deficit hyperactivity disorder (ADHD) and assist in its diagnosis  ...  Functional magnetic resonance imaging (fMRI) combined with fractional amplitude of low-frequency fluctuation (fALFF) are widely used in the research of ADHD [3] . fMRI is a reliable technique based on  ... 
doi:10.1109/access.2019.2915988 fatcat:abhvrp75ojdlbolxotim56zdna
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