Skip to main content
SpringerLink
Log in

Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The decoding of conscious experience, based on non-invasive measurements, has become feasible by tailoring machine learning techniques to analyse neuroimaging data. Recently, functional connectivity graphs (FCGs) have entered into the picture. In the related decoding scheme, FCGs are treated as unstructured data and, hence, their inherent format is overlooked. To alleviate this, tensor subspace analysis (TSA) is incorporated for the parsimonious representation of connectivity data. In addition to the particular methodological innovation, this work also makes a contribution at a conceptual level by encoding in FCGs cross-frequency coupling apart from the conventional frequency-specific interactions. Working memory related tasks, supported by networks oscillating at different frequencies, are good candidates for assessing the novel approach. We employed surface EEG recordings when the subjects were repeatedly performing a mental arithmetic task of five cognitive workload levels. For each trial, an FCG was constructed based on phase interactions within and between Frontalθ and Parieto-Occipitalα2 neural activities, which are considered to reflect the function of two distinct working memory subsystems. Based on the TSA representation, a remarkably high correct-recognition-rate (96%) of the task difficulties was achieved using a standard classifier. The overall scheme is computational efficient and therefore potentially useful for real-time and personalized applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Notes

  1. The peaks of θ (6 Hz) cycles were detected by estimating zero crossings from positive to negative values of the derivative of neural activity originating from frontal brain areas and filtered around 6 Hz. The FZ sensor served for deriving a consistent indicator for the last θ cycle in all trials and subjects.

  2. \( \overline{PLV} \) was estimated as the average PLV value within specific sub-graphs (Fθ and PΟα2) as were previously defined and were topologically presented in Fig. 3.

References

  1. Amiri, Setare, Reza Fazel-Rezai, and Vahid Asadpour. A review of hybrid brain-computer interface systems. Adv. Hum. Comput. Interact. 2013:1, 2013.

    Article  Google Scholar 

  2. Axmacher, N., M. M. Henseler, O. Jensen, I. Weinreich, C. E. Elger, and J. Fell. Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proc. Natl. Acad. Sci. USA 107:3228–3233, 2010.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Benjamini, Y., and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R Stat. Soc. Ser. B. (Methodological) 57(1):289–300, 1995.

    Google Scholar 

  4. Benz, H. L., H. Zhang, A. Bezerianos, S. Acharya, N. E. Crone, X. Zheng, and N. V. Thakor. Connectivity analysis as a novel approach to motor decoding for prosthesis control. IEEE Trans. Neural Syst. Rehabil. Eng. 20:143–152, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Blankertz, B., M. Tangermann, C. Vidaurre, S. Fazli, C. Sannelli, S. Haufe, C. Maeder, L. Ramsey, I. Sturm, G. Curio, and K. R. Muller. The Berlin brain-computer interface: non-medical uses of Bci technology. Front. Neurosci. 4:198, 2010.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Butnik, S. M. Neurofeedback in adolescents and adults with attention deficit hyperactivity disorder. J. Clin. Psychol. 61:621–625, 2005.

    Article  PubMed  Google Scholar 

  7. Chik, D. Theta-alpha cross-frequency synchronization facilitates working memory control—a modeling study. SpringerPlus 2:14, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Cichocki, A., R. Zdunek, A. H. Phan, and S. Amari. Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Chichester: Wiley, 2009.

    Book  Google Scholar 

  9. Daffertshofer, A. and C. J. Stam. Influences of volume conduction on phase distributions. In: International Congress Series, 2007, pp. 209–12.

  10. Daly, I., S. J. Nasuto, and K. Warwick. Brain computer interface control via functional connectivity dynamics. Pattern Recognit. 45:2123–2136, 2012.

    Article  Google Scholar 

  11. Delorme, A., and S. Makeig. Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. J. Neurosci. Methods 134:9–21, 2004.

    Article  PubMed  Google Scholar 

  12. Deshpande, G., Z. Li, P. Santhanam, C. D. Coles, M. E. Lynch, S. Hamann, and X. Hu. Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS ONE 5:e14277, 2010.

    Article  PubMed Central  PubMed  Google Scholar 

  13. Dimitriadis, S. I., N. A. Laskaris, V. Tsirka, M. Vourkas, S. Micheloyannis, and S. Fotopoulos. Tracking brain dynamics via time-dependent network analysis. J. Neurosci. Methods 193:145–155, 2010.

    Article  PubMed  Google Scholar 

  14. Dimitriadis, S. I., K. Kanatsouli, N. A. Laskaris, V. Tsirka, M. Vourkas, and S. Micheloyannis. Surface eeg shows that functional segregation via phase coupling contributes to the neural substrate of mental calculations. Brain Cogn. 80:45–52, 2012.

    Article  PubMed  Google Scholar 

  15. Dimitriadis, S. I., N. A. Laskaris, A. Tzelepi, and G. Economou. Analyzing functional brain connectivity by means of commute times: a new approach and its application to track event-related dynamics. IEEE Trans. Biomed. Eng. 59:1302–1309, 2012.

    Article  CAS  PubMed  Google Scholar 

  16. Dimitriadis, S. I., N. A. Laskaris, and A. Tzelepi. On the quantization of time-varying phase synchrony patterns into distinct functional connectivity microstates (fcmustates) in a multi-trial visual Erp paradigm. Brain Topogr. 26:397–409, 2013.

    Article  CAS  PubMed  Google Scholar 

  17. Dimitriadis, S. I., Y. Sun, K. Kwok, N. A. Laskaris, and A. Bezerianos. A tensorial approach to access cognitive workload related to mental arithmetic from eeg functional connectivity estimates. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013:2940–2943, 2013.

    CAS  PubMed  Google Scholar 

  18. Elgendi, M., J. Dauwels, B. Rebsamen, R. Shukla, Y. Putra, J. Gamez, N. ZePing, B. Ho, N. Prasad, and D. Aggarwal. From auditory and visual to immersive neurofeedback: application to diagnosis of Alzheimer’s disease. In Neural Computation, Neural Devices, and Neural Prosthesis, 2014, pp. 63–97.

  19. Fell, J., and N. Axmacher. The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 12:105–118, 2011.

    Article  CAS  PubMed  Google Scholar 

  20. Fisher, R. A. The use of multiple measurements in taxonomic problems. Ann. Eug. 7:179–188, 1936.

    Article  Google Scholar 

  21. Fisher, N. I. Statistical Analysis of Circular Data. Cambridge: Cambridge University Press, 1995.

    Google Scholar 

  22. Haynes, J. D., and G. Rees. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7:523–534, 2006.

    Article  CAS  PubMed  Google Scholar 

  23. He, X, D. Cai, and P. Niyogi. Tensor subspace analysis. In: Advances in neural information processing systems, 2005, pp. 499–506.

  24. Horikawa, T., M. Tamaki, Y. Miyawaki, and Y. Kamitani. neural decoding of visual imagery during sleep. Science 340:639–642, 2013.

    Article  CAS  PubMed  Google Scholar 

  25. Ioannides, A. A., S. I. Dimitriadis, G. A. Saridis, M. Voultsidou, V. Poghosyan, L. Liu, and N. A. Laskaris. Source space analysis of event-related dynamic reorganization of brain networks. Comput. Math. Methods Med. 2012:452503, 2012.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Kawasaki, M., K. Kitajo, and Y. Yamaguchi. Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. Eur. J. Neurosci. 31:1683–1689, 2010.

    PubMed Central  PubMed  Google Scholar 

  27. Klimesch, W. Eeg alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Brain Res. Rev. 29:169–195, 1999.

    Article  CAS  PubMed  Google Scholar 

  28. Klimesch, W., R. Freunberger, P. Sauseng, and W. Gruber. A short review of slow phase synchronization and memory: evidence for control processes in different memory systems? Brain Res. 1235:31–44, 2008.

    Article  CAS  PubMed  Google Scholar 

  29. Kohlmorgen, J., G. Dornhege, M. Braun, B. Blankertz, K. -R. Müller, G. Curio, K. Hagemann, A. Bruns, M. Schrauf, and W. Kincses. Improving human performance in a real operating environment through real-time mental workload detection. Toward Brain Comput. Interfacing. 409–422, 2007.

  30. Kotsiantis, Sotiris, Dimitris Kanellopoulos, and Panayiotis Pintelas. Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30:25–36, 2006.

    Google Scholar 

  31. Latchoumane, C. F., F. B. Vialatte, J. Sole-Casals, M. Maurice, S. R. Wimalaratna, N. Hudson, J. Jeong, and A. Cichocki. Multiway array decomposition analysis of EEGs in Alzheimer’s disease. J. Neurosci. Methods 207:41–50, 2012.

    Article  PubMed  Google Scholar 

  32. Lee, T. S., S. J. Goh, S. Y. Quek, R. Phillips, C. Guan, Y. B. Cheung, L. Feng, S. S. Teng, C. C. Wang, Z. Y. Chin, H. Zhang, T. P. Ng, J. Lee, R. Keefe, and K. R. Krishnan. A brain-computer interface based cognitive training system for healthy elderly: a randomized control pilot study for usability and preliminary efficacy. PLoS ONE 8:e79419, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Leonardi, N., and D. Van de Ville. Identifying network correlates of brain states using tensor decompositions of whole-brain dynamic functional connectivity. In: International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2013, pp. 74–77.

  34. Liang, N. Y., P. Saratchandran, G. B. Huang, and N. Sundararajan. Classification of mental tasks from eeg signals using extreme learning machine. Int. J. Neural Syst. 16:29–38, 2006.

    Article  PubMed  Google Scholar 

  35. Lim, C. G., T. S. Lee, C. Guan, D. S. Fung, Y. Zhao, S. S. Teng, H. Zhang, and K. R. Krishnan. A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS ONE 7:e46692, 2012.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Liu, T., J. Wang, Y. Chen, R. Wang, and M. Song. Neurofeedback treatment experimental study for ADHD by Using the Brain-Computer Interface Neurofeedback System. In: World Congress on Medical Physics and Biomedical Engineering, May 26–31, 2012, Beijing, China, 1537–1540, 2013.

  37. Luck, S. J., and E. K. Vogel. Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends Cogn. Sci. 17:391–400, 2013.

    Article  PubMed Central  PubMed  Google Scholar 

  38. Micheloyannis, S., E. Pachou, C. J. Stam, M. Vourkas, S. Erimaki, and V. Tsirka. Using graph theoretical analysis of multi channel eeg to evaluate the neural efficiency hypothesis. Neurosci. Lett. 402:273–277, 2006.

    Article  CAS  PubMed  Google Scholar 

  39. Muller, K. R., M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz. Machine learning for real-time single-trial eeg-analysis: from brain-computer interfacing to mental state monitoring. J. Neurosci. Methods 167:82–90, 2008.

    Article  PubMed  Google Scholar 

  40. Nunez, P. L., and R. Srinivasan. Electric Fields of the Brain: The Neurophysics of EEG. Oxford: Oxford university press, 2006.

    Book  Google Scholar 

  41. Rebsamen, B., K. Kwok, and T. B. Penney. Evaluation of cognitive workload from EEG during a mental arithmetic task. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2011, pp. 1342–1345.

  42. Richiardi, J., H. Eryilmaz, S. Schwartz, P. Vuilleumier, and D. Van De Ville. Decoding brain states from fMRI connectivity graphs. Neuroimage 56:616–626, 2011.

    Article  PubMed  Google Scholar 

  43. Samek, W., F. C. Meinecke, and K. R. Muller. Transferring subspaces between subjects in brain-computer interfacing. IEEE Trans. Biomed. Eng. 60:2289–2298, 2013.

    Article  PubMed  Google Scholar 

  44. Sauseng, P., W. Klimesch, M. Schabus, and M. Doppelmayr. Fronto-parietal eeg coherence in theta and upper alpha reflect central executive functions of working memory. Int. J. Psychophysiol. 57:97–103, 2005.

    Article  PubMed  Google Scholar 

  45. Sauseng, P., W. Klimesch, W. R. Gruber, and N. Birbaumer. Cross-frequency phase synchronization: a brain mechanism of memory matching and attention. Neuroimage 40:308–317, 2008.

    Article  PubMed  Google Scholar 

  46. Shen, H., L. Wang, Y. Liu, and D. Hu. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of FMRI. Neuroimage 49:3110–3121, 2010.

    Article  PubMed  Google Scholar 

  47. Smith, M. E., A. Gevins, H. Brown, A. Karnik, and R. Du. Monitoring task loading with multivariate eeg measures during complex forms of human-computer interaction. Hum. Factors 43:366–380, 2001.

    Article  CAS  PubMed  Google Scholar 

  48. Stam, C. J., G. Nolte, and A. Daffertshofer. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. 28:1178–1193, 2007.

    Article  PubMed  Google Scholar 

  49. Tajar, J., and S. Sharifi. Dyscalculia: learning disabilites in mathematice and treatment with teaching remedial method iranian children 6 years old. J. Appl. Sci. Res. 7(9):891–896, 2011.

  50. Tallon-Baudry, C., O. Bertrand, C. Delpuech, and J. Permier. Oscillatory gamma-band (30–70 Hz) activity induced by a visual search task in humans. J. Neurosci. 17:722–734, 1997.

    CAS  PubMed  Google Scholar 

  51. Thompson, L., M. Thompson, and A. Reid. Neurofeedback outcomes in clients with Asperger’s syndrome. Appl. Psychophysiol. Biofeedback 35:63–81, 2010.

    Article  PubMed  Google Scholar 

  52. Todd, J. J., and R. Marois. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428:751–754, 2004.

    Article  CAS  PubMed  Google Scholar 

  53. Wang, Q., and O. Sourina. Real-time mental arithmetic task recognition from EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 21:225–232, 2013.

    Article  PubMed  Google Scholar 

  54. Zander, T. O., and C. Kothe. Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J. Neural Eng. 8:025005, 2011.

    Article  PubMed  Google Scholar 

  55. Zhang, H., H. L. Benz, N. V. Thakor, and A. Bezerianos. Connectivity mapping of human brain by phase based evolution map approach. Int. J. Bifurc. Chaos 22:143–152, 2012.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under Grant Number of R-719-001-102-232. This work was partially supported by a Temasek Laboratories Research Collaboration Grant (R-581-000-093-422) awarded to T.B. Penney. We acknowledge the assistance of Brice Rebsamen in collection of EEG data.

Author information

Authors and Affiliations

  1. Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124, Thessaloniki, Greece

    Stavros I. Dimitriadis & Nikolaos A. Laskaris

  2. NeuroInformatics.GRoup, AUTH, Thessaloniki, Greece

    Stavros I. Dimitriadis & Nikolaos A. Laskaris

  3. Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore

    Yu Sun, Nitish Thakor & Anastasios Bezerianos

  4. Temasek Laboratories, National University of Singapore, Singapore, 117411, Singapore

    Kenneth Kwok

Authors
  1. Stavros I. Dimitriadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenneth Kwok
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolaos A. Laskaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nitish Thakor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anastasios Bezerianos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasios Bezerianos.

Additional information

Associate Editor Leonidas D Iasemidis oversaw the review of this article.

Stavros I. Dimitriadis and Yu Sun have contributed equally to this work.

Appendix: Algorithmic steps of the TSA

Appendix: Algorithmic steps of the TSA

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dimitriadis, S.I., Sun, Y., Kwok, K. et al. Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions. Ann Biomed Eng 43, 977–989 (2015). https://doi.org/10.1007/s10439-014-1143-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-014-1143-0

Keywords

Search

Navigation