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Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

C. Davatzikos, K. Ruparel, Y. Fan, D.G. Shen, M. Acharyya, J.W. Loughead, R.C. Gur, D.D. Langleben
2005 NeuroImage  
High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with  ...  The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical  ...  This collection of measurements constituted a sample of the spatial activation pattern. Customary to the machine learning terminology, we call these 560 values features.  ... 
doi:10.1016/j.neuroimage.2005.08.009 pmid:16169252 fatcat:itr3yydc2naitpb5w2llmzcwza

Concealed Information Detection Using EEG for Lie Recognition by ERP P300 in Response to Visual Stimuli: a Review

Martina Zabcikova, Zuzana Koudelkova, Roman Jasek
2022 WSEAS Transactions on Information Science and Applications  
The article contains a detailed overview of the methods used in scientific research in EEG-based lie detection using the ERP P300 component in response to known and unknown faces.  ...  This paper's main objective was to give an overview of the scientific works on the recognition of concealed information using EEG for lie detection in response to visual stimuli of faces, as there is no  ...  These techniques have enabled researchers to create applications based on a better understanding of brain activity.  ... 
doi:10.37394/23209.2022.19.17 fatcat:nypzrqstlbgqxanl3ofdgvdpja

An Efficient Method for Mining Event-Related Potential Patterns [article]

Seyed Aliakbar Mousavi, Muhammad Rafie Hj Arshad, Hasimah Hj Mohamed, Saleh Ali Alomari
2012 arXiv   pre-print
The aim for this research is to develop an infrastructure for mining, analysis and sharing the ERP domain ontologies. The outcome of this research is a Neuroelectromagnetic knowledge-based system.  ...  In the present paper, we propose a Neuroelectromagnetic Ontology Framework (NOF) for mining Event-related Potentials (ERP) patterns as well as the process.  ...  We thank Professor Jafri Malin Abdullah Department of Neuroscience, School of Medical Sciences and Prof. Rahmat Budiarto for helping us in this paper.  ... 
arXiv:1201.6112v1 fatcat:4ykf7bj5hzcyzncwvngycbl3zy

Reproducibility of importance extraction methods in neural network based fMRI classification

Athanasios Gotsopoulos, Heini Saarimäki, Enrico Glerean, Iiro.P. Jääskeläinen, Mikko Sams, Lauri Nummenmaa, Jouko Lampinen
2018 NeuroImage  
The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification.  ...  Highlights Successful inter-subject classification at whole-brain level with neural network classifiers Classification accuracy is proportional to effect size Comparison of importance extraction methods  ...  Acknowledgements We thank Marita Kattelus for her help with the data acquisition. We also acknowledge the computational resources provided by the Aalto Science-IT project.  ... 
doi:10.1016/j.neuroimage.2018.06.076 pmid:29964190 fatcat:hl32bdpghzfklgfhwpuii4bo2a

Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad
2021 IEEE Access  
PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods.  ...  This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods  ...  ACKNOWLEDGMENT The authors would like to appreciate the support of Universiti Kebangsaan Malaysia (UKM).  ... 
doi:10.1109/access.2021.3062709 fatcat:u5xr6p4ubbeollvfmo7l5mgu6a

Reproducibility of importance extraction methods in neural network based fMRI classification [article]

Athanasios Gotsopoulos, Heini Saarimaki, Enrico Glerean, Iiro P. Jaaskelainen, Mikko Sams, Lauri Nummenmaa, Jouko Lampinen
2017 bioRxiv   pre-print
We conclude that importance maps are superior to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.  ...  The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification.  ...  Acknowledgements We thank Marita Kattelus for her help with the data acquisition. We also acknowledge the computational resources provided by the Aalto Science-IT project.  ... 
doi:10.1101/197277 fatcat:hke6vd2dfja5rg6z2ruhclafnq

Alzheimer Disease Detection Techniques and Methods: A Review

Sitara Afzal, Muazzam Maqsood, Umair Khan, Irfan Mehmood, Hina Nawaz, Farhan Aadil, Oh-Young Song, Yunyoung Nam
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning.  ...  Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging.  ...  of oxygen during resting and active state to form an activity pattern.  ... 
doi:10.9781/ijimai.2021.04.005 fatcat:yklogr5wefei7e247dmjdrosum

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  ...  of general machine learning, which is composed of feature extraction, classifier design, classifier optimization, and evaluation with training and testing data.  ... 
doi:10.1007/s13534-011-0021-z fatcat:kxp7bsgscfc3hkn73nbgt6sfea

Spatio-Temporal Data Mining: A Survey of Problems and Methods [article]

Gowtham Atluri, Anuj Karpatne, Vipin Kumar
2017 arXiv   pre-print
Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern  ...  The presence of these attributes introduces additional challenges that needs to be dealt with.  ...  stamps with similar maps of brain activity [Liu and Duyn 2013] .  ... 
arXiv:1711.04710v2 fatcat:di3fxigwobeb3db5kcdvlhbe7i

A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method

Nirmalya Thakur, Chia Y. Han
2021 Journal of Sensor and Actuator Networks  
First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method  ...  This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in  ...  and to ensure the safety of the user.  ... 
doi:10.3390/jsan10030039 fatcat:ycdwrr55pffzrdk6vnd6flfepq

Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

Keum-Shik Hong, M. Jawad Khan, Melissa J. Hong
2018 Frontiers in Human Neuroscience  
The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed.  ...  First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them.  ...  Common spatial patterns Common spatial patterns (CSP) for EEG feature extraction and classification are used to project the multi-channel EEG data into a low-dimensional spatial subspace with a projection  ... 
doi:10.3389/fnhum.2018.00246 pmid:30002623 pmcid:PMC6032997 fatcat:ymtk4ovahzd3plimezfta7yr7y

IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering

Yu-Dong Zhang, Zhengchao Dong, Juan Manuel Gorriz, Yizhang Jiang, Ming Yang, Shui-Hua Wang
2021 IEEE Access  
She has served as a Visiting Scholar for the Department of Radiology, University of Maryland from January 2013 to June 2014.  ...  She is currently a Pediatric Committee Member of the Chinese Society of Radiology and the Chinese Medical Doctor Association of Radiology.  ...  ., proposes a novel method to classify single chromosome images into one of 24 types.  ... 
doi:10.1109/access.2021.3080355 fatcat:oez6u3npt5ff7aw7tscwyvlmvq

Video Bioinformatics Methods for Analyzing Cell Dynamics: A Survey [chapter]

Nirmalya Ghosh
2015 Computational Biology  
Computational tools from established fields like computer vision, pattern recognition, and machine learning have immensely improved quantification at different stages-from image preprocessing and cell  ...  segmentation to cellular feature extraction and selection, classification into different phenotypes, and exploration of hidden content-based patterns in bioimaging databases.  ...  Conclusions Automated analysis of microscopic bioimages is making significant progresses with application of established tools and algorithms from computer vision, pattern recognition, and machine learning  ... 
doi:10.1007/978-3-319-23724-4_2 fatcat:wjsaagwnpbgmziy662tsmw6hv4

Classification methods for ongoing EEG and MEG signals

2007 Biological Research  
In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g.  ...  Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification.  ...  ACKNOWLEDGEMENTS The authors would like to thank Dr Mario Chavez and Fréderique Amor for their useful comments on the manuscript.  ... 
doi:10.4067/s0716-97602007000500005 fatcat:3ti4dbwpmjgzrd5pwoplqnevwy

Robust modeling based on optimized EEG bands for functional brain state inference

Ilana Podlipsky, Eti Ben-Simon, Talma Hendler, Nathan Intrator
2012 Journal of Neuroscience Methods  
The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research.  ...  The regression is then used to derive a model of frequency distributions that identifies brain states.  ...  In the field of EEG-based Brain-Computer Interface (BCI) design, machine learning algorithms are used to identify 'patterns' of brain activity that identify a certain mental task (Anderson et al., 1998  ... 
doi:10.1016/j.jneumeth.2011.10.015 pmid:22044846 fatcat:rss5bchr7jbxdplymnc35xmbqe
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