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| EEG Signal Complexity Analysis Based on Multi-Frequency Permutation Entropy |
| Niu Yan1, Gao Kai1, Ding Runan1, Wen Xin2, Zhou Mengni3, Xiang Jie1* |
1(College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China)
2(School of Software, Taiyuan University of Technology, Taiyuan 030024, China)
3(Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China) |
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Abstract Complexity analysis is of great importance in electroencephalography (EEG) signal studies. Multivariate entropy methods have proven to be effective techniques for analyzing signal complexity, but existing multivariate entropy studies have set the variables as multichannel time series. The quantification of brain dynamics' complexity from a multi-frequency analysis perspective has not been widely explored. By improving the multivariate permutation entropy (mvPE) algorithm, this study proposed multi-frequency permutation entropy (mFPE) to provide a more detailed measure of brain complexity in the time-frequency dimension. The study analyzed the performance of the algorithm using simulated data and three sets of real EEG data. Firstly, the performance of the mFPE algorithm was analyzed using 1/f noise and Gaussian white noise, as well as simulated data generated by the MIX model. It was found that mFPE had higher sensitivity, shorter data length requirements and good anti-noise performance compared to mvPE. Applying the mFPE algorithm to the analysis of EEG data from 14 Parkinson′s patients and 14 healthy controls, mFPE was able to significantly differentiate between normal and patient brain activity and achieved 78.7% classification accuracy, which was superior to mvPE (72.8%). Secondly, using EEG data from 14 patients with depressive tendencies and 14 healthy controls also revealed a 6.6% improvement in accuracy with mFPE compared to mvPE. Finally, using visual task EEG data from 32 normal subjects, mFPE effectively revealed the changes in EEG activity induced by different task stimuli, and the classification accuracies for different tasks were also higher than those of mvPE. This study showed that the mFPE algorithm provided a new perspective and an effective tool for the dynamic analysis of EEG signal complexity, which is expected to play an important role in the fields of neurological disease diagnosis, brain function research and cognitive science.
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Received: 16 April 2024
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