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Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform

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Abstract

For the brain-computer interface system (BCI), pre-processing has an important role to ensure system performance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporal effects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and non-stationary characteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of the system for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. In many cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the input signal. In this paper, we proposed a heuristic approach to finding the optimal filter of the system for EEG signals. The harmony search algorithm (HSA) was used for finding of the optimal filter. In the learning process with the EEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, Chung-Ang University, 221, Heukseok-dong, Dongjak-gu, Seoul, 156-756, Korea

    Seung-Min Park, Tae-Ju Lee & Kwee-Bo Sim

Authors
  1. Seung-Min Park
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  2. Tae-Ju Lee
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  3. Kwee-Bo Sim
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Corresponding author

Correspondence to Kwee-Bo Sim.

Additional information

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Euntai Kim. This research was supported by the National Research Foundation of KOREA [NRF] grant funded by the KOREA government [MEST] [2012-0008726].

Seung-Min Park received his B.S. and M.S. degrees from the Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2010 and 2012, respectively. He is currently a candidate for the Ph.D. degree in the School of Electrical and Electronics Engineering at Chung-Ang University. His research interests include pattern recognition, brain computer interface, intention recognition and deep learning.

Tae-Ju Lee received his B.S. and M.S. degrees in Electrical and Electronics Engineering from Chung-Ang University, in 2013 and 2015. His research interests include brain-computer interface (BCI), intention recognition, neuro-robotics, and soft computing etc.

Kwee-Bo Sim received his B.S. and M.S. degrees from the Department of Electronic Engineering, Chung-Ang University, Korea, in 1984 and 1986, respectively. He received his Ph.D. from the Department of Electrical and Electronics Engineering at the University of Tokyo, Japan, in 1990. Since 1991, he has been a faculty member of the School of Electrical and Electronics Engineering at Chung-Ang University, where he is currently a professor. His research interests are artificial life, intelligent robots, intelligent systems, multi-agent systems, distributed autonomous robotic systems, machine learning, adaptation algorithms, soft computing (neural networks, fuzzy systems, and evolutionary computation), artificial immune systems, evolvable hardware, artificial brain technology, intelligent homes, home networking, intelligent sensors, and ubiquitous computing. He is a member of the IEEE, SICE, RSJ, IEICE, KITE, KIEE, KIIS, and an ICROS Fellow.

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Park, SM., Lee, TJ. & Sim, KB. Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform. Int. J. Control Autom. Syst. 14, 1582–1587 (2016). https://doi.org/10.1007/s12555-016-0031-9

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  • DOI: https://doi.org/10.1007/s12555-016-0031-9

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