Machine Learning Approaches and Neuroimaging in Cognitive Functions of the Human Brain: A Review

  • Siamak AramEmail author
  • Denis Kornev
  • Roozbeh Sadeghian
  • Saeed Esmaili Sardari
  • Sagar Kora Venu
  • Hadis Dashtestani
  • Amir Gandjbakhche
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)


Brain science is that sphere of knowledge on the frontline of modern reality wherefrom the accuracy of diagnoses and speed of decision making depends on human mental health. Machine Learning and Deep Learning are the contemporary methodologies and algorithms that can combine a huge amount of complex data in the coherent structure and help scientists solve brain disorders. This paper reviews different Machine Learning algorithms that investigate data patterns and trends, collected from the human brain using several neuroimaging techniques.


Functional near-infrared spectroscopy Deep Learning Cognitive functions Machine Learning Neuroimaging 


  1. 1.
    Jiang, F., Jiang, Y., Zhi, H., Dong, Yi., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., Wang, Y.: Artificial intelligence in healthcare: past, present, and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)
  2. 2.
    Zhu, G., Jiang, B., Tong, L., Xie, Y., Zaharchuk, G., Wintermark, M.: Applications of deep learning to neuro-imaging techniques. Frontiers Neurol. 10, 869 (2019)
  3. 3.
    McCarthy, J.: What is Artificial Intelligence? (2017).
  4. 4.
    Mazurowski, M.A., Buda, M., Saha, A., Bashir, M.R.: Deep learning in radiology: an overview of the concepts and a survey of the state of the art. J. Mag. Reson. Imaging 49(4), 1–27 (2018)
  5. 5.
    Kamal, H., Lopez, V., Sheth, S.A.: Machine learning in acute ischemic stroke neuroimaging. Frontiers Neurol. 9, 945 (2018)
  6. 6.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Mach. Learn. 20, 273–297 (1995)
  7. 7.
    Rehme, A.K., Volz, L.J., Feis, D.L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S.B., Fink, G.R., Grefkes, C.: Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb. Cortex 25, 3046–3056 (2015)
  8. 8.
    Haller, S., Badoud, S., Nguyen, D., Garibotto, V., Lovblad, K.O., Burkhard, P.R.: Individual detection of patients with parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. Am. J. Neuroradiol. 33(11), 2123–2128 (2012)
  9. 9.
    Li, R., Rui, G., Chen, W., Li, S., Schulz, P.E., Zhang, Y.: Early detection of Alzheimer’s disease using non-invasive near-infrared spectroscopy. Frontiers Aging Neurosci. 10, 366 (2018)
  10. 10.
    Quaresima, V., Ferrari, M.: A mini-review on functional near-infrared spectroscopy (fNIRS): where do we stand, and where should we go? Photonics 6(3), 87 (2019)
  11. 11.
    Bunce, S., Izzetoglu, K., Izzetoglu, M., Onaral, K., Banu, O., Kambiz, P.: Functional near-infrared spectroscopy. IEEE Eng. Med. Biol. Mag. 25(4), 54–62 (2006)
  12. 12.
    Friston, K.J.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2(1–2), 56–78 (1994)
  13. 13.
    Rojas, R.F., Huang, X., Ou, K.L.: A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci. Rep. 9(1), 5645 (2019)
  14. 14.
    Karamzadeh, N., Amyot, F., Kenney, K., Anderson, A., Chowdhry, F., Dashtestani, H., Wassermann, E.M., Chernomordik, V., Boccara, C., Wegman, E., Diaz-Arrastia, R., Gandjbakhche, A.H.: A machine learning approach to identify functional biomarkers in the human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy. Brain Behav. 6(11), 1–14 (2016)
  15. 15.
    Lopez-Martinez, D., Peng, K., Lee, A., Borsook, D., Picard, R.: Pain detection with fNIRS-measured brain signals: a personalized machine learning approach using the wavelet transform and bayesian hierarchical modeling with dirichlet process priors. In: 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) (2019)
  16. 16.
    Ho, T.K.K., Gwak, J., Park, C.M., Song, J.I.: Discrimination of mental workload levels from multi-channel fNIRS using deep learning-based approaches. IEEE Access 7, 24392–24403 (2019)
  17. 17.
    Hiwa, S., Hanawa, K., Tamura, R., Hachisuka, K., Hiroyasu1, T.: Analyzing brain functions by subject classification of functional near-infrared spectroscopy data using convolutional neural networks analysis. Comput. Intell. Neurosci. 2016, 1–9 (2016)
  18. 18.
    Benerradi, J., Maior, H.A., Marinescu, A., Clos, J., Wilson, M.L.: Exploring machine learning approaches for classifying mental workload using fNIRS data from HCI tasks. In: Proceedings of the Halfway to the Future Symposium. ACM (2019)
  19. 19.
    Trakoolwilaiwan, T., Behboodi, B., Lee, J., Kim, K., Choi, J.W.: Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution. Neurophotonics 5(1), 011008-1–15 (2017)
  20. 20.
    Dashtestani, H., Zaragoza, R., Kermanian, R., Knutson, K.M., Halem, M., Casey, A., Karamzadeh, N.S., Anderson, A.A., Boccara, A.C., Gandjbakhche, A.: The role of prefrontal cortex in a moral judgment task using functional near-infrared spectroscopy. Brain Behav. 8, 1–10 (2018)
  21. 21.
    Venu1, S.K., Sadeghian, R., Sardari, S.E., Dashtestani, H., Gandjbakhche, A., Aram, S.: Neural correlates of brain activities in gaming using functional near-infrared spectroscopy and Iowa gambling task. In: Abstracts of 11th International Conference on Physical Ergonomics and Human Factors (2019)
  22. 22.
    Sarraf, S., Tofighi, G.: Deep Learning-Based Pipeline To Recognize Alzheimer’s disease using fMRI data. In: Future Technologies Conference, pp. 1–5 (2016)
  23. 23.
    Thomas, A.W., Heekeren, H.R., Müller, K.R., Samek, W.: Interpretable LSTMs for whole-brain neuroimaging analyses, pp. 1–26 (2018)
  24. 24.
    Fong, R.C., Scheirer, W.J., Cox, D.D.: Using human brain activity to guide machine learning. Sci. Rep. 8(5397), 1–10 (2018)
  25. 25.
    Jeong, J.H., Yu, B.W., Lee, D.H., Lee, S.W.: Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional LSTM network using electroencephalography signals. Brain Sci. 9(348), 1–18 (2019)
  26. 26.
    Al Zoubi, O., Ki Wong, C., Kuplicki, R.T., Yeh, H.W., Mayeli, A., Refai, H., Paulus, M., Bodurka, J.: Predicting age from brain EEG signals-a machine learning approach. Frontiers Aging Neurosci. 10(184), 1–12 (2018)
  27. 27.
    Mumtaza, W., Xiad, L., AzharAlia, S.S., Mohd Yasinb, M.A., Hussainc, M., Malika, A.S.: Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed. Signal Process. Control 31, 108–115 (2017)
  28. 28.
    Saadati, M., Nelson, J., Ayaz, H.: Multimodal fNIRS-EEG classification using deep learning algorithms for brain-computer interfaces purposes. In: Advances in Intelligent Systems and Computing, pp. 209–220 (2019)
  29. 29.
    Shin, J., Kwon, J., Im, C.H.: A ternary hybrid EEG-NIRS brain-computer interface for the classification of brain activation patterns during mental arithmetic, motor imagery, and idle state. Frontiers Neuroinf. 12(5), 1–9 (2018)
  30. 30.
    Sirpal, P., Kassab, A., Pouliot, P., Nguyen, D.K., Lesage, F.: fNIRS improves seizure detection in multimodal EEG-fNIRS recordings. J. Biomed. Opt. 24(5), 1–9 (2019)
  31. 31.
    Dargazany, A.R., Abtahi, M., Mankodiya, K.: An end-to-end (Deep) neural network applied to raw EEG, fNIRS, and body motion data for data fusion and BCI classification task without any pre-/post-processing, pp. 1–6 (2019)

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Siamak Aram
    • 1
    Email author
  • Denis Kornev
    • 1
  • Roozbeh Sadeghian
    • 1
  • Saeed Esmaili Sardari
    • 1
  • Sagar Kora Venu
    • 1
  • Hadis Dashtestani
    • 2
  • Amir Gandjbakhche
    • 2
  1. 1.Harrisburg University of Science and TechnologyHarrisburgUSA
  2. 2.Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of HealthBethesdaUSA

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