Abstract
The concept of functional brain networks offers new and interesting avenues for studying human brain function. One such avenue, as described in the current paper, involves spanning subgraphs called Minimum Connected Components (MCC) that contain only the influential connections of such networks. This paper investigates cognitive load driven changes across different brain regions using these MCC sub-graphs constructed for different states of brain functioning under different degrees of cognitive load using the graph theoretic concept of clique. The presence of cliques signifies cohesive interconnections among the subsets of nodes in MCC that are tightly knit together. To further characterise the cognitive load state from that of the baseline state, the hemisphere wise interactions among the electrode sites are measured. The empirical analysis presented in this paper demonstrates the efficiency of the MCC based clique analysis in detecting and measuring cognitive activity with the technique presented potentially having application in the clinical diagnosis of cognitive impairments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sporns, O., Tononi, G., Kotter, R.: The human connectome: A structural description of the human brain. PLOS ONE. 1 (4) (2005)
Power, J.D., Cohen, A.L., Nelsom, S.M., Wig, G.S., Barnes, K.A., Church, J.A.: Functional network organization of the human brain. Neuron 72, 665–678 (2011)
Vijayalakshmi, R., Nandagopal, D., Dasari, N., Cocks, B., Thilaga, M.: Minimum connected component - a novel approach to detection of cognitive load induced changes in functional brain networks. Neurocomputing (2015). doi:10.1016/j.neucom.2015.03.092
Nunez, P.L.: Electroencephalography. Encyclopedia of Human. Brain 2(2), 1348 (2002)
Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques and computational experiments. Theor. Comput. Sci. 363(1), 28–42 (2006)
Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Eppstein, D., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 364–375. Springer, Heidelberg (2011)
Eppstein, D., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 364–375. Springer, Heidelberg (2011)
Bron, C., Kerbosch, J.: Algorithm 457: Finding all cliques of an undirected graph. Commun. ACM 16(9), 575–577 (1973)
Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques and computational experiments. Theor. Comput. Sci. 363(1), 28–42 (2006)
Faust, K., Wasserman, S.: Social network analysis: Methods and applications. Cambridge University Press, Cambridge (1995)
Boginski, V., Butenko, S., Pardalos, P.M.: Statistical analysis of financial networks. Comput. Stat. Data Anal. 48(2), 431–443 (2005)
Dumermuth, G.: Quantification and analysis of the EEG. Schweiz. Arch. Neurol. Neurochir. Psychiatr. 115(2), 175–192 (1974)
Delorme, A., Makeig, S.: EEGLAB: An open source toolbox for the analysis of single-trial EEG dynamics including independent component analysis. NCBI 134(1), 9–21 (2004)
Nandagopal, D., Vijayalakshmi, R., Cocks, B., Dahal, N., Dasari, N., Thilaga, M.: Computational neuroengineering Approaches to characterising cognitive activity in EEG data. Knowl. Based Inf. Syst. Pract. Smart Innovation Knowl. Syst. Technol. 30, 115–137 (2015)
He, Y., Evans, A.: Graph theoretical modeling of brain connectivity. Curr. Opin. Neurol. 23, 341–350 (2010)
CURRY 7 EEG Acquisition and Analysis Software. Compumedics Neuroscan USA Ltd
Acknowledgement
This work is being supported by Cognitive NeuroEngineering Laboratory (CNeL), University of South Australia, Adelaide, Australia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vijayalakshmi, R., Nandagopal, D., Thilaga, M., Cocks, B. (2015). Characterisation of Cognitive Activity Using Minimum Connected Component. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-26561-2_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26560-5
Online ISBN: 978-3-319-26561-2
eBook Packages: Computer ScienceComputer Science (R0)