Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

  • Lee, Ki-Baek (Dept. of Electrical Engineering, Kwangwoon University) ;
  • Kim, Ko Keun (Intelligence Lab., Convergence Center at LG Electronics) ;
  • Song, Jaeseung (Dept. of Computer and Information Security, Sejong University) ;
  • Ryu, Jiwoo (Dept. of Computer Engineering, Kwangwoon University) ;
  • Kim, Youngjoo (Dept. of Computer Engineering, Kwangwoon University) ;
  • Park, Cheolsoo (Dept. of Computer Engineering, Kwangwoon University)
  • Received : 2015.05.27
  • Accepted : 2016.07.21
  • Published : 2016.11.01
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Abstract

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

Keywords

Acknowledgement

Grant : The Development of oneM2M Conformance Testing Tool and QoS Technology

Supported by : National Research of Korea(NRF), Institute for Information & communications Technology Promotion(IITP)

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