Optimal partial filters of EEG signals for shared control of vehicle

Won Gil Huh; Sung Bae Cho

doi:10.1109/SOCPAR.2015.7492823

Optimal partial filters of EEG signals for shared control of vehicle

Won Gil Huh, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

The development of equipment that measures EEG signals leads to the research that applies them to many domains. There are active research going on EEG signals for shared vehicle control system between human and car. An appropriate filtering method is also important because EEG signals normally have lots of noises. To reduce such noises, full matrix filter, sparse matrix reference filter, and common average reference (CAR) filter are presented and analyzed in this paper. In order to develop shared vehicle control system, we use controller, brain-computer interface (BCI), EEG signals, and car simulator program. By executing t-test, it was possible to find the optimal filter out of three filters mentioned above. With the analysis of t-test, it has revealed that full matrix filter is not appropriate for shared vehicle control system. In addition, it proves CAR filter has the best performance among these filters.

Original language	English
Title of host publication	Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
Editors	Mario Koppen, Azah Kamilah Muda, Kun Ma, Bing Xue, Hideyuki Takagi, Ajith Abraham
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	290-293
Number of pages	4
ISBN (Electronic)	9781467393607
DOIs	https://doi.org/10.1109/SOCPAR.2015.7492823
Publication status	Published - 2016 Jun 15
Event	7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan Duration: 2015 Nov 13 → 2015 Nov 15

Publication series

Name	Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

Other

Other	7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
Country/Territory	Japan
City	Fukuoka
Period	15/11/13 → 15/11/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Signal Processing
Control and Optimization
Modelling and Simulation

Access to Document

10.1109/SOCPAR.2015.7492823

Cite this

Huh, W. G., & Cho, S. B. (2016). Optimal partial filters of EEG signals for shared control of vehicle. In M. Koppen, A. K. Muda, K. Ma, B. Xue, H. Takagi, & A. Abraham (Eds.), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 (pp. 290-293). Article 7492823 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SOCPAR.2015.7492823

Huh, Won Gil ; Cho, Sung Bae. / Optimal partial filters of EEG signals for shared control of vehicle. Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. editor / Mario Koppen ; Azah Kamilah Muda ; Kun Ma ; Bing Xue ; Hideyuki Takagi ; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 290-293 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).

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abstract = "The development of equipment that measures EEG signals leads to the research that applies them to many domains. There are active research going on EEG signals for shared vehicle control system between human and car. An appropriate filtering method is also important because EEG signals normally have lots of noises. To reduce such noises, full matrix filter, sparse matrix reference filter, and common average reference (CAR) filter are presented and analyzed in this paper. In order to develop shared vehicle control system, we use controller, brain-computer interface (BCI), EEG signals, and car simulator program. By executing t-test, it was possible to find the optimal filter out of three filters mentioned above. With the analysis of t-test, it has revealed that full matrix filter is not appropriate for shared vehicle control system. In addition, it proves CAR filter has the best performance among these filters.",

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Huh, WG & Cho, SB 2016, Optimal partial filters of EEG signals for shared control of vehicle. in M Koppen, AK Muda, K Ma, B Xue, H Takagi & A Abraham (eds), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015., 7492823, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Institute of Electrical and Electronics Engineers Inc., pp. 290-293, 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Fukuoka, Japan, 15/11/13. https://doi.org/10.1109/SOCPAR.2015.7492823

Optimal partial filters of EEG signals for shared control of vehicle. / Huh, Won Gil; Cho, Sung Bae.
Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. ed. / Mario Koppen; Azah Kamilah Muda; Kun Ma; Bing Xue; Hideyuki Takagi; Ajith Abraham. Institute of Electrical and Electronics Engineers Inc., 2016. p. 290-293 7492823 (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The development of equipment that measures EEG signals leads to the research that applies them to many domains. There are active research going on EEG signals for shared vehicle control system between human and car. An appropriate filtering method is also important because EEG signals normally have lots of noises. To reduce such noises, full matrix filter, sparse matrix reference filter, and common average reference (CAR) filter are presented and analyzed in this paper. In order to develop shared vehicle control system, we use controller, brain-computer interface (BCI), EEG signals, and car simulator program. By executing t-test, it was possible to find the optimal filter out of three filters mentioned above. With the analysis of t-test, it has revealed that full matrix filter is not appropriate for shared vehicle control system. In addition, it proves CAR filter has the best performance among these filters.

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Huh WG, Cho SB. Optimal partial filters of EEG signals for shared control of vehicle. In Koppen M, Muda AK, Ma K, Xue B, Takagi H, Abraham A, editors, Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 290-293. 7492823. (Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015). doi: 10.1109/SOCPAR.2015.7492823

Optimal partial filters of EEG signals for shared control of vehicle

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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