Classification of Cognitive States Using Clustering-Split Time Series Framework


Over the last two decades, functional Magnetic Resonance Imaging (fMRI) has provided immense data about the dynamics of the brain. Ongoing developments in machine learning suggest improvements in the performance of fMRI data analysis. Clustering is one of the critical techniques in machine learning. Unsupervised clustering techniques are utilized to partition the data objects into different groups. Supervised classification techniques applied to fMRI data facilitate the decoding of cognitive states while a subject is engaged in a cognitive task. Due to the high dimensional, sparse, and noisy nature of fMRI data, designing a classifier model for estimating cognitive states becomes challenging. Feature selection and feature extraction techniques are critical aspects of fMRI data analysis. In this work, we present one such synergy, a combination of Hierarchical Consensus Clustering (HCC) and the Statistics of Split Timeseries (SST) framework to estimate cognitive states. The proposed HCC-SST model’s performance has been verified on StarPlus fMRI data. The obtained experimental results show that the proposed classifier model achieves 99% classification accuracy with a smaller number of voxels and lower computational cost.


functional MRI data, classification, consensus clustering, SVM classifier, GNB classifier, XGBoost,


1. O.J. Arthurs, S. Boniface, How well do we understand the neural origins of the fMRI BOLD signal, Trends in Neurosciences, 25(1): 27–31, 2002, doi: 10.1016/S0166-2236(00)01995-0.
2. N.K. Logothetis, B.A. Wandell, Interpreting the BOLD signal,Annual Reviews in Physiology, 66: 735–769, 2004, doi: 10.1146/annurev.physiol.66.082602.092845.
3. T.M. Mitchell, R. Hutchinson, M.A. Just, R.S. Niculescu, F. Pereira, X. Wang, Classifying instantaneous cognitive states from fMRI data, [in:] American Medical Informatics Association Annual Symposium Proceedings, pp. 465–469, 2003, PMID: PMC1479944,
4. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80: 23983–24004, 2021, doi: 10.1007/s11042-021-10840-0.
5. A. Kishor, C. Chakarbarty, Task offloading in fog computing for using smart ant colony optimization, Wireless Personal Communications, 127: 1683–1704, 2021, doi: 10.1007/s11277-021-08714-7.
6. F. De Martino, G. Valente, N. Staeren, J. Ashburner, R. Goebel, E. Formisano, Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns, Neuroimage, 43(1): 44–58, 2008, doi: 10.1016/j.neuroimage.2008.06.037.
7. A.L. Fred, A.K. Jain, Combining multiple clusterings using evidence accumulation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6): 835–850, 2005, doi: 10.1109/TPAMI.2005.113.
8. S. Ryali, T. Chen, A. Padmanabhan, W. Cai, V. Menon, Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI, Journal of Neuroscience Methods, 240: 128–140, 2015, doi: 10.1016/j.jneumeth.2014.11.014.
9. J. Tang, S. Alelyani, H. Liu, Feature selection for classification: A review, [in:] Data Classification: Algorithms and Applications, C.C. Aggarwal [Ed.], CRC Press, New York, pp. 37–64, 2014.
10. J.D. Cohen et al., Computational approaches to fMRI analysis, Nature Neuroscience, 20(3): 304–313, 2017, doi: 10.1038/nn.4499.
11. D.R. Hardoon, J. Mourão-Miranda, M. Brammer, J. Shawe-Taylor, Unsupervised analysis of fMRI data using kernel canonical correlation, NeuroImage, 37(4): 1250–1259, 2007, doi: 10.1016/j.neuroimage.2007.06.017.
12. R. Xu, D. Wunsch, Survey of clustering algorithms, IEEE Transactions on Neural Networks, 16(3): 645–678, 2005, doi: 10.1109/TNN.2005.845141.
13. R. Agrawal, C. Faloutsos, A. Swami, Efficient similarity search in sequence databases, [in:] Lecture Notes in Computer Science, Vol. 730, pp. 69–84, 1993, doi: 10.1007/3-540-57301-1_5.
14. S. Monti, P. Tamayo, J. Mesirov, T. Golub, Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data, Machine Learning, 52(1): 91–118, 2003, doi: 10.1023/A:1023949509487.
15. K. Kim, R.I. McKay, B.R. Moon, Multi objective evolutionary algorithms for dynamic social network clustering, [in:] GECCO ’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1179–1186, 2010, doi: 10.1145/1830483.1830699.
16. V. Michel, C. Damon, B. Thirion, Mutual information-based feature selection enhances fMRI brain activity classification, [in:] 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, pp. 592–595, 2008, doi: 10.1109/ISBI.2008.4541065.
17. M. Yang, K. Kpalma, J. Ronsin, A survey of shape feature extraction techniques, [in:] Pattern Recognition Techniques, Technology and Applications, P.Y. Yin [Ed.], I-Tech, Vienna, Austria, pp. 43–90, 2008, doi: 10.5772/6237.
18. A. Eklund, M. Andersson, H. Knutsson, fMRI analysis on the GPU—Possibilities and challenges, Computer Methods and Programs in Biomedicine, 105(2): 145–161, 2012, doi: 10.1016/j.cmpb.2011.07.007.
19. S.M. Smith, A. Hyvärinen, G. Varoquaux, K.L. Miller, C.F. Beckmann, Group-PCA for very large fMRI datasets, NeuroImage, 101: 738–749, 2014, doi: 10.1016/j.neuroimage.2014.07.051.
20. X. Ma, C.A. Chou, H. Sayama, W.A. Chaovalitwongse, Brain response pattern identification of fMRI data using a particle swarm optimization-based approach, Brain Informatics, 3(3): 181–192, 2016, doi: 10.1007/s40708-016-0049-z.
21. H. Shahamat, A.A. Pouyan, Feature selection using genetic algorithm for classification of schizophrenia using fMRI data, Journal of AI and Data Mining, 3(1): 30–37, 2015, doi: 10.5829/idosi.JAIDM.2015.03.01.04.
22. K.O. Gupta, P.N. Chatur, Cognitive state classification using genetic algorithm based linear collaborative discriminant regression, [in:] 2018 First International Conference on Secure Cyber Computing and Communication, Jalandhar, India, pp. 180–183, 2018, doi: 10.1109/ICSCCC.2018.8703280.
23. M. Fan, C.A. Chou, Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: A comprehensive study, Brain Informatics, 3: 193–203, 2016, doi: 10.1007/s40708-016-0048-0.
24. A. Ranjan, V.P. Singh, A.K. Singh, A.K. Thakur, R.B. Mishra, Classifying brain state in sentence polarity exposure: An ANN model for fMRI data, Revue d’Intelligence Artificielle, 34(3): 361–368, 2020, doi: 10.18280/ria.340315.
25. Y. Shi, W. Zeng, N. Wang, L. Zhao, A new constrained spatiotemporal ICA method based on multi-objective optimization for fMRI data analysis, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(9): 1690–1699, 2018, doi: 10.1109/TNSRE.2018.2857501.
26. A.W. Thomas, K.R. Müller, W. Samek, Deep transfer learning for whole-brain fMRI analyses, [in:] OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. Lecture Notes in Computer Science, L. Zhou et al. [Eds.], Vol. 11796, Springer, Cham, pp. 59–67, 2019, doi: 10.1007/978-3-030-32695-1_7.
27. M. Salehi, A. Karbasi, D.S. Barron, D. Scheinost, R.T. Constable, Individualized functional networks reconfigure with cognitive state, NeuroImage, 206: 116233, 2020, doi: 10.1016/j.neuroimage.2019.116233.
28. K.H. Madsen, L.G. Krohne, X.L. Cai, Y.Wang, R.C. Chan, Perspectives on machine learning for classification of schizotypy using fMRI data, Schizophrenia Bulletin, 44(suppl_ 2): S480–S490, 2018, doi: 10.1093/schbul/sby026.
29. Y. Lin et al., Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks, NeuroImage, 230: 117791, 2021, doi: 10.1016/j.neuroimage.2021.117791.
30. Y. Zhang, L. Tetrel, B. Thirion, P. Bellec, Functional annotation of human cognitive states using deep graph convolution, NeuroImage, 231: 117847, 2021, doi: 10.1016/j.neuroimage.2021.117847.
31. StarPlus fMRI data, 2001,
32. J.S. Ramakrishna, H. Ramasangu, Cognitive state classification using clustering-classifier hybrid method, [in:] 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, pp. 1880–1885, 2016, doi: 10.1109/ICACCI.2016.7732324.
33. J.S. Ramakrishna, H. Ramasangu, Classification of cognitive state using statistics of split time series, [in:] 2016 IEEE Annual India Conference (INDICON), Bangalore, India, pp. 1–5, 2016, doi: 10.1109/INDICON.2016.7839078.
Apr 8, 2024
How to Cite
RAMAKRISHNA, J. Siva; RAMASANGU, Hariharan. Classification of Cognitive States Using Clustering-Split Time Series Framework. Computer Assisted Methods in Engineering and Science, [S.l.], v. 31, n. 2, p. 241–260, apr. 2024. ISSN 2956-5839. Available at: <>. Date accessed: 18 july 2024. doi:
[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems