IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

EEG-based Customized Driving Control Model Design

뇌파를 이용한 맞춤형 주행 제어 모델 설계

  • Received : 2022.11.15
  • Accepted : 2023.03.29
  • Published : 2023.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부에서 지원하는 대구경북과학기술원 기관 고유사업 (23-IT-02) 지원을 받아 수행 되었습니다.

References

  1. M. L. Martini, E. K. Oermann, N. L. Opie, F. Panov, T. Oxley, K. Yaeger, "Sensor Modalities for Brain-computer Interface Technology: A Comprehensive Literature Review," Neurosurgery, Vol. 86, No. 2, pp. E108-E117, 2020. https://doi.org/10.1093/neuros/nyz286
  2. J. Meng, S. Zhang, A. Bekyo, J. Olsoe, B. Baxter, B. He, "Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks," Scientific Reports, Vol. 6, 38565, 2016.
  3. K. Korovesis, D. Kandris, G. Koulouras, A. Alexandridis, "Robot Motion Control via an EEG-Based Brain- Computer Interface by Using Neural Networks and Alpha Brainwaves," Electronics, Vol. 8, No. 12, 1387, 2019.
  4. H. H. Jo, S. C. Jeon, "EEG based Brain-Computer Interface Technology", Information and Communications Magazine, Vol. 29, No. 7, pp. 47-55, 2012 (in Korean).
  5. D. E. Kim, T. J. Lee, S. M. Park, "EEG Analysis Following Change in Hand Grip Force Level for BCI Based Robot Arm Force Control", J. Korean. Inst. Intel. Sys., Vol. 23, No. 2, pp. 172-177, 2013 (in Korean). https://doi.org/10.5391/JKIIS.2013.23.2.172
  6. S. H. Seo, "Development of Deep Learning Model in Brain Signal Classification for BCI Control", Journal of Next-generation Convergence Technology Association, Vol. 4, No. 4, pp. 471-477, 2020 (in Korean). https://doi.org/10.33097/JNCTA.2020.04.05.471
  7. S. Saha, K. A. Mamun, K. Ahmed, R. Mostafa, G. R. Naik, S. Darvishi, A. H. Khandoker, M. Baumert, "Progress in Brain Computer Interface: Challenges and Opportunities," Frontiers in Systems Neuroscience, Vol. 15, 578875, 2021.
  8. D. S. Bolme, J. R. Beveridge, B. A. Draper, Y. M. Lui, "Visual Object Tracking Using Adaptive Correlation Filters," IEEE Computer Society Conference on Computer Vision and Pattern Recognit, pp. 2544-2550, 2010.
  9. J. S. Yun, J. H. Kim, "A Study on EEG Artifact Removal Method Using Eye Tracking Sensor Data," J. Inst. Korean. Electr. Electron. Eng, Vol. 22, No. 4, pp.1109-1114, 2018 (in Korean).
  10. J. Chang, B. Kim, C. Mun, D. Lee, J. Kwakl, "Efficient Hyperplane Generation Techniques for Human Activity Classification in Multiple-Event Sensors Based Smart Home", IEMEK J. Embed. Sys. Appl., Vol. 14, No. 5, pp. 277-286, 2019 (in Korean).
  11. H. Jung, M. K. Choi, J. Kim, S. Kwon, W. Y. Jung, "CNN-based Weighted Ensemble Technique for ImageNet Classification," IEMEK J. Embed. Sys. Appl., Vol. 15, No. 4, pp. 197-204, 2020 (in Korean).
  12. D. Sabbagh, P. Ablin, G. Varoquaux, A. Gramfort, D. A. Engemann, "Predictive Regression Modeling with MEG/EEG: from Source Power to Signals and Cognitive States," Jour. NeuroImage, Vol. 222, 2020.
  13. C. Pan, C. Shi, H. Mu, J. Li, X. Gao, "EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands," Jour. Applied Sciences, Vol. 10, No. 5, 2020.
  14. A. X. Stewart, A. Nuthmann, G. Sanguinetti, "Single-trial Classification of EEG in a Visual Object Task Using ICA and Machine Learning," Journal of Neuroscience Methods, Vol. 228, pp. 1-14, 2014.  https://doi.org/10.1016/j.jneumeth.2014.02.014