Abstract
Extraction of brain patterns from electroencephalography signals to discriminate brain states has been an important research field to the develop of non-invasive applications like brain-computer-interface systems or diagnosis of neurodegenerative diseases. However, most of the state-of-the-art methodologies use observations derived from each electrode independently, without considering the possible dependencies between channels. To improve understanding of brain functionality, connectivity analysis have been developed. Nevertheless in those works, where connectivity measures are included, the total number of connections is high dimensional, and the relevance of connectivity values is not considered. To cope with this issue, we propose a kernel-based inter-channel connectivity relevance analysis (termed ConnRA), for such a purpose, linear dependencies between channel signals are extracted using coherence measures over specific sub-frequency bands, and a similarity criterion is implemented to rank the contribution of each channel-to-channel connection for a specific task. Experimental validation carried out on a database of brain-computer interfaces, demonstrate very promising results, making the proposed methodology a suitable alternative to support many neurophysiological applications.
J.V. Hurtado-Rincón—This research is supported by Programa Jóvenes Investigadores e Innovadores convocatoria 645-2014 Manizales funded by Colciencias and Universidad Nacional de Colombia, it is also supported by COLCIENCIAS project Evaluación asistida de potenciales evocados cognitivos como marcador del transtorno por déficit de atención e hiperactividad (TDAH) and Programa Nacional de Formacion de Investigadores “Generacion del Bicentenario”, 2011. The authors also thank to “Maestría en Ingeniería Eléctrica” and research project “6-14-1” at “Universidad Tecnológica de Pereira” for the financial support.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
http://bbci.de/competition/iv/desc_1.html. BCI competition IV 2008, Dataset 1.
References
Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interface: a review. In: Hassanien, A.B., Azar, A.T. (eds.) Brain-Computer Interfaces, vol. 74, pp. 3–30. Springer, Heidelberg (2012)
Faust, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26, 56–64 (2015)
Maglione, A.G., Vecchiato, G., Babiloni, F.: On the use of cognitive neuroscience in industrial applications by using neuroelectromagnetic recordings. In: Liljenström, H. (ed.) Advances in Cognitive Neurodynamics (IV), pp. 31–37. Springer, Heidelberg (2013)
Ingber, L., Nunez, P.L.: Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs. Math. Biosci. 229(2), 160–173 (2011)
Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Nat. Acad. Sci. 91(11), 5033–5037 (1994)
Stam, C.J., Van Dijk, B.W.: Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Phys. D: Nonlinear Phenom. 163(3), 236–251 (2002)
Lithari, C., Klados, M.A., Bamidis, P.D.: Graph analysis on functional connectivity networks during an emotional paradigm. In: Bamidis, P.D., Pallikarakis, N. (eds.) XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, vol. 29, pp. 115–118. Springer, Heidelberg (2010)
Kim, S.-P., Chung, Y.G., Kim, M.-K.: Inter-channel connectivity of motor imagery EEG signals for a noninvasive bci application. In: International Workshop on Pattern Recognition in NeuroImaging. IEEE (2011)
Chen, M., Han, J., Guo, L., Wang, J., Patras I.: Identifying valence and arousal levels via connectivity between EEG channels. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 63–69. IEEE (2015)
Gupta, R., Falk, T.H., et al.: Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174, 875–884 (2016)
Srinivasan, R., Winter, W.R., Ding, J., Nunez, P.L.: EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods 166(1), 41–52 (2007)
Brockmeier, A.J., Choi, J.S., Kriminger, E.G., Francis, J.T., Principe, J.C.: Neural decoding with kernel-based metric learning. Neural Comput. 26(6), 1080–1107 (2014)
Velasquez-Martinez, F., Alvarez-Meza, A.M., Castellanos-Dominguez, G.: Connectivity analysis of motor imagery paradigm using short-time features and kernel similarities. In: Vicente, J.M.F., Álvarez-Sánchez, J.R., Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) Artificial Computation in Biology and Medicine. LNCS, vol. 9107, pp. 439–448. Springer, Heidelberg (2015)
He, W., Wei, P., Wang, L., Zou Y.: A novel emd-based common spatial pattern for motor imagery brain-computer interface. In: IEEE EMBC (2012)
Álvarez-Meza, A.M., Velásquez-Martínez, L.F., Castellanos-Dominguez, G.: Time-series discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151, 122–129 (2015)
Rodríguez, G., García, P.J.: Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces. Med. Syst. 36(1), 51–63 (2012)
Zhang, H., Guan, C., Ang, K.K., Wang, C.: BCI competition iv-data set i: learning discriminative patterns for self-paced EEG-based motor imagery detection. Front. Neurosci. 6, 7 (2012)
Haufe, S., Nikulin, V.V., Müller, K.-R., Nolte, G.: A critical assessment of connectivity measures for eeg data: a simulation study. Neuroimage 64, 120–133 (2013)
Murias, M., Swanson, J.M., Srinivasan, R.: Functional connectivity of frontal cortex in healthy and ADHD children reflected in EEG coherence. Cereb. Cortex 17(8), 1788–1799 (2007)
Rodrak, S., Wongsawat, Y.: EEG brain mapping and brain connectivity index for subtypes classification of attention deficit hyperactivity disorder children during the eye-opened period. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7400–7403. IEEE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Hurtado-Rincón, J.V., Martínez-Vargas, J.D., Rojas-Jaramillo, S., Giraldo, E., Castellanos-Dominguez, G. (2016). Identification of Relevant Inter-channel EEG Connectivity Patterns: A Kernel-Based Supervised Approach. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-47103-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47102-0
Online ISBN: 978-3-319-47103-7
eBook Packages: Computer ScienceComputer Science (R0)