Support vector machine classification of complex fMRI data

In this paper an application of genetic algorithms (GAs) and Gaussian Naïve Bayesian (GNB) approach is studied to explore the brain activities by decoding specific cognitive states from functional magnetic resonance imaging (fMRI) data. However, in case of fMRI data analysis the large number of attributes may leads to a serious problem of classifying cognitive states. It significantly increases the computational cost and memory usage of a classifier. Hence to address this problem, we use GAs for selecting optimal set of attributes and then GNB classifier in a pipeline to classify different cognitive states. The experimental outcomes prove its worthiness in successfully classifying different cognitive states. The detailed comparison study with popular machine learning classifiers illustrates the importance of such GA-Bayesian approach applied in pipeline for fMRI data analysis.

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“…The experiment consists of a series of brain images which is being collected for class label changes [16].…”

Section: B Methods For Comparative Studymentioning

confidence: 99%

Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification

Parida¹,

Dehuri

Cho

2014

2014 IEEE International Advance Computing Conference (IACC)

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“…Specifically, we describe the results of a neuroimaging study using an ''on-off'' fMRI finger-tapping experiment, which models the brain response via an oscillatory stepwise characteristic function with a delayed correlation to the on-off stimulus paradigm. The observed data represents complex-valued fMRI time-series of the blood oxygenation dependent (BOLD) response to a dichotomous finger-tapping ''on versus off'' (activation vs. rest) eventrelated experiment [38,39]. The raw signal is acquired in the Fourier k-space over a period of 8-min tracking the functional BOLD signal of a normal volunteer during the finger-tapping ''on-off'' task.…”

Section: Datasetsmentioning

confidence: 99%

Kimesurface representation and tensor linear modeling of longitudinal data

Zhang

Liu

et al. 2022

Neural Comput & Applic

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Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the timecourses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive reals to the complex plane. Using complex time (kime), we transform classical time-varying signals as 2D manifolds called kimesurfaces. This kime characterization extends the classical protocols for analyzing time-series data and offers unique opportunities to design novel inference, prediction, classification, and regression techniques based on the corresponding kimesurface manifolds. We define complex time and illustrate alternative time-series to kimesurface transformations. Using the Laplace transform and its inverse, we demonstrate the bijective mapping between time-series and kimesurfaces. A proposed general tensor regression based linear model is validated using functional Magnetic Resonance Imaging data. This kimesurface representation method can be used with a wide range of machine learning algorithms, artificial intelligence tools, analytical approaches, and inferential techniques to interrogate multivariate, complex-domain, and complex-range longitudinal processes.

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“…SVM classifier is a type of supervised machine learning approach that attempts to distinguish between two classes of data points separated by a hyperplane in a high dimensional space ( Cortes and Vapnik, 1995 ; Chen et al, 2020 ). SVM is widely used to deal with classification problems in machine learning ( Byvatov et al, 2003 ; Peltier et al, 2009 ), many studies of lie detection ( Mottelson et al, 2018 ; Mazza et al, 2020 ; Mathur and Matarić, 2020 ) or eye movements ( Huang et al, 2015 ; Dalrymple et al, 2019 ; Steil et al, 2019 ; Kang et al, 2020 ) have used SVM for classification.…”

Section: Study 2: Spontaneous Liementioning

confidence: 99%

Assessing Deception in Questionnaire Surveys With Eye-Tracking

et al. 2021

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Deceit often occurs in questionnaire surveys, which leads to the misreporting of data and poor reliability. The purpose of this study is to explore whether eye-tracking could contribute to the detection of deception in questionnaire surveys, and whether the eye behaviors that appeared in instructed lying still exist in spontaneous lying. Two studies were conducted to explore eye movement behaviors in instructed and spontaneous lying conditions. The results showed that pupil size and fixation behaviors are both reliable indicators to detect lies in questionnaire surveys. Blink and saccade behaviors do not seem to predict deception. Deception resulted in increased pupil size, fixation count and duration. Meanwhile, respondents focused on different areas of the questionnaire when lying versus telling the truth. Furthermore, in the actual deception situation, the linear support vector machine (SVM) deception classifier achieved an accuracy of 74.09%. In sum, this study indicates the eye-tracking signatures of lying are not restricted to instructed deception, demonstrates the potential of using eye-tracking to detect deception in questionnaire surveys, and contributes to the questionnaire surveys of sensitive issues.

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Support vector machine classification of complex fMRI data

Cited by 15 publications

References 3 publications

Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification

Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification

Kimesurface representation and tensor linear modeling of longitudinal data

Assessing Deception in Questionnaire Surveys With Eye-Tracking

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Support vector machine classification of complex fMRI data

Cited by 15 publications

References 3 publications

Application of genetic algorithms and Gaussian Na&#x00EF;ve Bayesian approach in pipeline for cognitive state classification

Application of genetic algorithms and Gaussian Na&#x00EF;ve Bayesian approach in pipeline for cognitive state classification

Kimesurface representation and tensor linear modeling of longitudinal data

Assessing Deception in Questionnaire Surveys With Eye-Tracking

Contact Info

Product

Resources

About

Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification

Application of genetic algorithms and Gaussian Naïve Bayesian approach in pipeline for cognitive state classification