2 年 之前
Understanding and tracking the transition of brain activity is a well-known problem in the field of brain science. The nonlinear time series analysis, including Recurrence quantification analysis (RQA), permutation entropy, and permutation conditional mutual information, is proposed to describe the dynamical characteristics of EEG (electroencephalograph) recordings. One of the advantages of these methods do not require any assumptions about EEG data, such as linear, stationary, noiseless, and so on.
More details on tools can be found on the Matlab File Exchange website:
ww2.mathworks.cn/matlabcentral/profile/authors/3517855
If you need permutation entropy codes that implement critical functions with (fast) C code. Download it here!
Referrences:
G Ouyang, X Li, C Dang, DA Richards, Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats, Clinical Neurophysiology 2008,119 (8), 1747-1755
G Ouyang, X Zhu, Z Ju, H Liu, Dynamical Characteristics of Surface EMG Signals of Hand Grasps via Recurrence Plot, IEEE Journal of Biomedical and Health Informatics, 2013,18 (1), 257 - 265
X Li, G Ouyang, D Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research, 2007, 77: 70-74
Ouyang, G., et al. Ordinal pattern based similarity analysis for EEG recordings. Clin Neurophysiol 2010, 121(5): 694-703.
X Li, G Ouyang, Estimating coupling direction between neuronal populations with permutation conditional mutual information, NeuroImage, 2010,52:497-507
G Ouyang, J Li, X Liu, X Li, Dynamic Characteristics of Absence EEG Recordings with Multiscale Permutation Entropy Analysis, Epilepsy Research, 2013, 104:246-252
