DocumentCode :
2554004
Title :
Efficacy of adaptive directed transfer function for neural connectivity estimation of EEG signal during meditation
Author :
Shaw, Laxmi ; Routray, Aurobinda
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kharagpur, Kharagpur, India
fYear :
2015
fDate :
19-20 Feb. 2015
Firstpage :
198
Lastpage :
202
Abstract :
Electroencephalogram (EEG) is widely used in cognitive science, neuroscience and physiological research. It is a good mean to observe cognitive response that depends on time. EEG has many advantages over other techniques owing to its non-invasiveness, low cost and high temporal resolution. But one of the major challenges of EEG signal study is the huge data dimensionality which makes signal processing and subsequent analysis an uphill task. The aim of this study is to obtain a model for better neural connectivity analysis which illustrates meditation´s dynamic mind-body response. Accordingly the EEG data is being collected during meditation (Kriya Yoga). In order to calculate and visualize the time-frequency representations of each electrode, a time varying Granger Causality based connectivity estimators named Directed Transfer Function (DTF) and adaptive DTF (ADTF) among all scalp electrodes have been computed in meditator group. The ADTF can be derived from the coefficients of a time-varying multivariate autoregressive (TVAR) model fitted to the data obtained during meditation. We define this time-varying measure of causality as the adaptive directed transfer function (ADTF) and compare its ability with the conventional DTF for meditator group. Both ADTF and Conventional DTF were calculated in meditator. The obtained simulation results of adaptive DTF and conventional DTF shows better neural connectivity and gives useful information in meditator group. However, to accomplish this task, surrogate data statistics has been used in both the mentioned models to validate the models. It was found that the ADTF has the capability to distinguish the dynamic changes in the primary source of the information outflow. The results obtained both by using ADTF and conventional DTF method were compared in meditator group subsequently.
Keywords :
adaptive signal detection; adaptive signal processing; biomedical electrodes; cognition; electroencephalography; filtering theory; medical signal processing; time-frequency analysis; ADTF capability; ADTF derivation; ADTF method-obtained results; EEG advantages; EEG data collection; EEG noninvasiveness; EEG signal analysis; EEG signal challenges; EEG signal neural connectivity estimation; EEG signal processing; EEG-based cognitive response observation; Kriya Yoga-collected EEG data; Kriya Yoga-collected electroencephalographic data; TVAR model coefficient-derived ADTF; adaptive DTF simulated results; adaptive directed transfer function capability; adaptive directed transfer function derivation; adaptive directed transfer function method-obtained results; cognitive science; conventional DTF ability; conventional DTF method-obtained results; conventional DTF simulated results; conventional directed transfer function ability; conventional directed transfer function method-obtained results; conventional directed transfer function simulated results; data-fitted TVAR model; dynamic information source changes; dynamic mind-body response; electroencephalogram; electroencephalographic data collection; electroencephalographic signal analysis; electroencephalographic signal challenges; electroencephalographic signal neural connectivity estimation; electroencephalographic signal processing; electroencephalography advantages; electroencephalography noninvasiveness; high EEG temporal resolution; high electroencephalographic temporal resolution; huge data dimensionality; low cost EEG; low cost electroencephalography; meditation activity-derived EEG signal; meditation activity-derived electroencephalographic signal; meditation-associated mind-body response; meditation-collected EEG data; meditation-collected electroencephalographic data; meditation-obtained data; meditator group; meditator-calculated ADTF; meditator-calculated conventional DTF; neural connectivity analysis model; neural connectivity information; neuroscience; noninvasive EEG; noninvasive electroencephalography; physiological research; primary information outflow source; primary information source changes; scalp electrode ADTF computation; scalp electrode time-frequency representation; statistics-validated models; surrogate data statistics; time-dependent cognitive response; time-frequency representation calculation; time-frequency representation visualization; time-varying Granger causality based connectivity estimators; time-varying causality measure; time-varying multivariate autoregressive model; Adaptation models; Analytical models; Brain modeling; Data models; Electroencephalography; Mathematical model; Transfer functions; Information flow; adaptive directed transfer function; connectivity measure; meditation; time frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-5990-7
Type :
conf
DOI :
10.1109/SPIN.2015.7095413
Filename :
7095413
Link To Document :
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