• Cao Phuong Thao University of Transport and Communications
  • Nguyen Thi Hau University of Transport and Communications
  • Nguyen Thanh Toan University of Transport and Communications
Keywords: eeg, autoregressive model, directed influence


Analyzing the interaction among electroencephalogram (EEG) data is an important step to understand the brain process. There have been many studies of directional effective influence between regions of the brain using EEG. However, most of the previous studies are based on the assumption of time-invariant connectivity structure, which is insufficient to understand the change of the connectivity during cognitive tasks. In this paper, we introduce the time-frequency method to analyzing the eeg data using autoregressive model. The method allows the examination of the dynamic interactions among brain regions during cognitive tasks using Kalman filter and Partial Directed Coherence. The result of the simulation study and the real data indicates that this approach is effective to estimate the time-varying connectivity among EEG data.


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How to Cite
Cao Phuong Thao, Nguyen Thi Hau, & Nguyen Thanh Toan. (2016). EEG ANALYSIS USING TIME-FREQUENCY SIGNAL PROCESSING TECHNIQUE. UTEHY Journal of Science and Technology, 11, 68-72. Retrieved from