TY - GEN
T1 - Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
AU - Marrouch, Natasza
AU - Read, Heather L.
AU - Slawinska, Joanna
AU - Giannakis, Dimitrios
N1 - Funding Information:
We thank Misako Komatsu, Kana Takaura, and Naotaka Fuji, and the RIKEN Brain Science Institute for making the data used in this paper available. N. M. acknowledges support from the CT Institute for the Brain and Cognitive Sciences Graduate Summer Fellowship and the University of Connecticut Department of Psychological Sciences’ Maurice L. Farber Endowment. H. R. acknowledges funding from NSF grant 1355065 and NIH DC015138 01 and the University of Connecticut Brain Computer Interface Core. J. S. acknowledges funding from NSF EAGER grant 1551489. D. G. received support from ONR YIP grant N00014-16-1-2649, NSF grant DMS-1521775, and DARPA grant HR0011-16-C-0116. The authors thank Ian Stevenson and Stephen Herzog for feedback on earlier drafts.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - This paper presents a data-driven method to extract spatiotemporal dynamics of mismatch negativity in a marmoset monkey. In this, we treat electrocorticographic (ECoG) data as observables of a skew-product dynamical system and extract the patterns of the neural dynamics from the point of view of the operator-theoretic formulation of ergodic theory. We successfully extract time-separable frequencies without bandpass filtering. Second, we examine in more detail the frequency band most commonly associated with MMN-beta-band activity (13-20 Hz) and proceed to cross-validate our results with those obtained by Komatsu, Takaura, and Fuji (2015). Having ensured the compatibility and statistical significance of the results, we then examine the spatiotemporal dynamics, and we find that MMN is in part driven by a synchronization in brain response following a deviation in the auditory stimuli.
AB - This paper presents a data-driven method to extract spatiotemporal dynamics of mismatch negativity in a marmoset monkey. In this, we treat electrocorticographic (ECoG) data as observables of a skew-product dynamical system and extract the patterns of the neural dynamics from the point of view of the operator-theoretic formulation of ergodic theory. We successfully extract time-separable frequencies without bandpass filtering. Second, we examine in more detail the frequency band most commonly associated with MMN-beta-band activity (13-20 Hz) and proceed to cross-validate our results with those obtained by Komatsu, Takaura, and Fuji (2015). Having ensured the compatibility and statistical significance of the results, we then examine the spatiotemporal dynamics, and we find that MMN is in part driven by a synchronization in brain response following a deviation in the auditory stimuli.
KW - Koopman operators
KW - electrophysiological data
KW - kernel methods
KW - mismatch negativity
KW - spatiotemporal patterns
UR - http://www.scopus.com/inward/record.url?scp=85056539727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056539727&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489475
DO - 10.1109/IJCNN.2018.8489475
M3 - Conference contribution
AN - SCOPUS:85056539727
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 July 2018 through 13 July 2018
ER -