Abstract
Neural information flow in brain during meditation, can be addressed by brain connectivity studies. This work aims to obtain neural connectivity measures based on a strictly causal time varying Multi-Variate Auto-Regressive (MVAR) model, fitted to EEG signals obtained during meditation. The time varying Granger Causality based connectivity estimators as PDC (Partial Directed Coherence), g-PDC (generalized Partial Directed Coherence), OPDC (Orthogonalized Partial Directed Coherence) and g-OPDC (generalized Orthogonalized Partial Directed Coherence) are calculated using the adaptive autoregressive MVAR parameters. The MVAR model parameters have been estimated by Kalman Filter algorithm. In this work g-PDC and g-OPDC have been used to make the connectivity measures scale invariant. These connectivity estimators quantify the neural information flow between Electroencephalograph (EEG) channels. In addition g-OPDC is also immune to volume conduction artifact and gives better result compared to g-PDC. Finally, surrogate data statistics has been used to check the significance of the above connectivity estimators.
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Shaw, L., Mishra, S., Routray, A. (2015). Generalised Orthogonal Partial Directed Coherence as a Measure of Neural Information Flow During Meditation. In: Gupta, S., Bag, S., Ganguly, K., Sarkar, I., Biswas, P. (eds) Advancements of Medical Electronics. Lecture Notes in Bioengineering. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2256-9_13
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DOI: https://doi.org/10.1007/978-81-322-2256-9_13
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