FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data
Pages 45433 - 45452
Abstract
Functional Magnetic Resonance Imaging (fMRI) is the popular technique where it is possible to capture neural activity in brain regions when subjected to different stimuli. However, due to fMRI datasets' high dimensional and sparse nature, the best features' selection plays an essential role in providing the best classification accuracy in fMRI models. This paper selects the stable feature set from the fMRI dataset using hybrid Fast Fourier Transform with Particle Swarm Optimization and Genetic Algorithm (FFTPSOGA). Fast Fourier Transform (FFT) is used on the extracted features by PSO-GA to convert the magnitude of features into phase values for better performance. Next, the machine learning algorithms of GaussianNB, Support Vector Machine (SVM), and XGboost has been trained based on these extracted features of six subjects of the dataset. The experimental analysis reveals that the proposed algorithm resulted in optimum features that helped extract informative Regions of Interest (ROI) with better classification accuracy. Our implemented algorithm FFTPSOGA extracted the best voxels in six subjects of the dataset by selecting minimum ROIs with a model classification accuracy of 0.98, 0.95, 0.95, 0.95, 0.97, and 0.96 for the SVM classifier. Comparison of the proposed scheme with state-of-the-art techniques show that our algorithm resulted in best voxels and outperformed work in [1, 9, 25] by achieving higher accuracy of 98% and low computational costs with only 127 number of features. Due to its better performance, we believe that it can be used for the pattern identification of brain responses in multi-subject fMRI data.
References
[1]
Albalawi F, Alshehri S, Chahid A, and Laleg-Kirati T M “voxel weight matrix-based feature extraction for biomedical applications” IEEE Access 2020 8 121451-121459
[2]
Anter AM, Wei Y, Su J, Yuan Y, Lei B, Duan G, and Fu Z A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI Inf Sci 2019 503 670-687
[3]
Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, and Han H Recommendation system using feature extraction and pattern recognition in clinical care systems Enterprise Inform Syst 2019 13 3 329-351
[4]
Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Mehmood A “Local Similarity-Based Spatial–Spectral Fusion Hyperspectral Image Classification with Deep CNN and Gabor Filtering”. IEEE Trans Geosci Remote Sens, vol. 60, pp. 1-15, 2021.
[5]
Chen JE and Glover GH Functional magnetic resonance imaging methods Neuropsychol Rev 2015 25 3 289-313
[6]
Cohen JD, Daw N, Engelhardt B, Hasson U, Li K, Niv Y, Norman KA, et al. Computational approaches to fMRI analysis Nat Neurosci 2017 20 3 304-313
[7]
Eklund A, Andersson M, and Knutsson H fMRI analysis on the GPU—possibilities and challenges Comput Methods Prog Biomed 2012 105 2 145-161
[8]
Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, and Calhoun VD Comparison of multi-subject ICA methods for analysis of fMRI data Hum Brain Mapp 2011 32 12 2075-2095
[9]
Fan M and Chou CA Exploring stability-based voxel selection methods in mvpa using cognitive neuroimaging data: a comprehensive study Brain Inform 2016 3 3 193-203
[10]
Fang Y, Liu J, Li J, Cheng J, Hu J, Yi D, and Bhatti UA Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT Multimed Tools Appl 2022 81 12 16863-16879
[11]
Ghamisi P and Benediktsson JA Feature selection based on hybridization of genetic algorithm and particle swarm optimization IEEE Geosci Remote Sens Lett 2014 12 2 309-313
[12]
Jin B, Strasburger A, Laken SJ, Kozel FA, Johnson KA, George MS, and Lu X Feature selection for fMRI-based deception detection BMC Bioinform 2009 10 9 1-7 BioMed Central
[13]
Kassraian-Fard P, Matthis C, Balsters JH, Maathuis MH, and Wenderoth N Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example Front Psychiatry 2016 no. 7 177
[14]
Kauttonen J, Hlushchuk Y, and Tikka P Optimizing methods for linking cinematic features to fMRI data Neuroimage 2015 110 136-148
[15]
Korhonen O, Saarimäki H, Glerean E, Sams M, and Saramäki J Consistency of regions of interest as nodes of fMRI functional brain networks Network Neurosci 2017 1 3 254-274
[16]
Lahiri R, Rakshit P, and Konar A Evolutionary perspective for optimal selection of EEG electrodes and features Biomed Signal Process Control 2017 36 113-137
[17]
Liu J, Ji J, Jia X, and Zhang A Learning brain effective connectivity network structure using ant colony optimization combining with voxel activation information IEEE J Biomed Health Inform 2019 24 7 2028-2040
[18]
Ma X, Chou CA, Sayama H, and Chaovalitwongse WA Brain response pattern identification of fMRI data using a particle swarm optimization-based approach Brain Inform 2016 3 3 181-192
[19]
Metawa N, Hassan MK, and Elhoseny M Genetic algorithm-based model for optimizing bank lending decisions Expert Syst Appl 2017 80 75-82
[20]
Michel V, Damon C, and Thirion B Mutual information-based feature selection enhances fMRI brain activity classification In 2008 5th IEEE international symposium on biomedical imaging: from nano to macro 2008 IEEE 592-595
[21]
Mirzaei S and Soltanian-Zadeh H Overlapping brain community detection using Bayesian tensor decomposition J Neurosci Methods 2019 318 47-55
[22]
Ota K, Oishi N, Ito K, and Fukuyama H Sead-J study group, & Alzheimer's disease neuroimaging Initiative. “Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease” J Neurosci Methods 2015 256 168-183
[23]
Paul S and Das S Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach Pattern Recogn Lett 2019 65 51-59
[24]
Poldrack RA The future of fMRI in cognitive neuroscience Neuroimage 2012 62 2 1216-1220
[25]
Ramakrishna JS, Ramasangu H Classification of cognitive state using clustering based maximum margin feature selection framework”, In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1092-1096. IEEE
[26]
Rashid M, Singh H, and Goyal V The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—a systematic review Expert Syst 2020 37 6 e12644
[27]
Satoru HIWA, Kohri Y, Hachisuka K, and Hiroyasu T Region-of-interest extraction of fMRI data using genetic algorithms In 2016 IEEE symposium series on computational intelligence (SSCI) 2016 IEEE 1-7
[28]
Sengupta S, Basak S, and Peters RA Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives Mach Learn Knowl Extrac 2019 1 1 157-191
[29]
Serra A, Galdi P, and Tagliaferri R Machine learning for bioinformatics and neuroimaging Wiley Interdisciplinary Rev: Data Mining Knowl Discov 2018 8 5 e1248
[30]
Shahamat H and Pouyan AA Feature selection using genetic algorithm for classification of schizophrenia using fMRI data J AI Data Mining 2015 3 1 30-37
[31]
Shi Y, Zeng W, Wang N, and Zhao L A new constrained spatiotemporal ICA method based on multi-objective optimization for fMRI data analysis IEEE Trans Neural Syst Rehab Eng 2018 26 9 1690-1699
[32]
Sidhu G Locally linear embedding and fMRI feature selection in psychiatric classification IEEE J Trans Eng Health Med 2019 7 1-11
[33]
Smith SM, Hyvärinen A, Varoquaux G, Miller KL, and Beckmann CF Group-PCA for very large fMRI datasets Neuroimage 2014 101 738-749
[34]
Song Y, Wang F, and Chen X An improved genetic algorithm for numerical function optimization Appl Intell 2019 49 5 1880-1902
[35]
Sumanaweera T and Liu D Medical image reconstruction with the FFT GPU Gems 2005 2 765-784
[36]
Tian D and Shi Z MPSO: modified particle swarm optimization and its applications Swarm Evol Comput 2018 41 49-68
[37]
Tom Mitchell WW: Starplus fmri data. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/
[38]
Wang Y, Ji J, and Liang P Feature selection of fMRI data based on normalized mutual information and fisher discriminant ratio J X-ray Sci Technol 2016 24 3 467-475
[39]
Xu W, Li Q, Liu X, Zhen Z, and Wu X Comparison of feature selection methods based on discrimination and reliability for fMRI decoding analysis J Neurosci Methods 2020 no. 335 108567
[40]
Yang Z, Zhuang X, Sreenivasan K, Mishra V, Cordes D, and Initiative A's DN Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network NeuroImage 2020 no. 223 117340
[41]
Young KS, Maj A, van der Velden MG, Craske KJ, Pallesen LF, Roepstorff A, and Parsons CE The impact of mindfulness-based interventions on brain activity: a systematic review of functional magnetic resonance imaging studies Neurosci Biobehav Rev 2018 84 424-433
[42]
Zeng C, Liu J, Li J, Cheng J, Zhou J, Nawaz SA, Bhatti UA (2022) Multi-watermarking algorithm for medical image based on KAZE-DCT. J Ambient Intell Human Comput:1–9
Index Terms
- FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data
Index terms have been assigned to the content through auto-classification.
Recommendations
Decoding visual brain states from fMRI using an ensemble of classifiers
Decoding perceptual or cognitive states based on brain activity measured using functional magnetic resonance imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and ...
Affirmative and Negative Sentence Detection in the Brain Using SVM-RFE and Rotation Forest: An fMRI Study
AbstractStudies on the brain’s response to positive and negative stimuli using non-invasive techniques such as fMRI and PET scans have been widely researched. The use of fMRI in particular, to study sentence polarity detection in the brain, has become a ...
Toward fMRI Group Identification Based on Brain Lateralization
WAINA '09: Proceedings of the 2009 International Conference on Advanced Information Networking and Applications WorkshopsThis research presents a novel application of Lateralization Index (LI) in support of a decision making process for the classification of subjects based on their brain activation patterns using Functional Magnetic Resonance Imaging (fMRI) datasets. The ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 02 May 2023
Accepted: 18 April 2023
Revision received: 17 December 2022
Received: 22 March 2022
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Jan 2025