뇌파를 이용한 맞춤형 주행 제어 모델 설계
EEG-based Customized Driving Control Model Design
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초록
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.
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.
참고문헌 (14)
- M. L. Martini, "Sensor Modalities for Brain-computer Interface Technology: A Comprehensive Literature Review", Neurosurgery, 86(2), pp.E108~E117 (2020)
- J. Meng, "Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks", Scientific Reports, 6, pp.38565~ (2016)
- K. Korovesis, "Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves", Electronics, 8(12), pp.1387~ (2019)
- H. H. Jo, "EEG based Brain-Computer Interface Technology", Information and Communications Magazine, 29(7), pp.47~55 (2012)
- 김동은, "BCI 기반 로봇 손 제어를 위한 악력 변화에 따른 EEG 분석", 한국지능시스템학회 논문지, 23(2), pp.172~177 (2013), 한국지능시스템학회
- 서쌍희, "BCI 제어를 위한 뇌신호 분류에서의 딥러닝 모델 개발", 차세대융합기술학회논문지, 4(5), pp.471~477 (2020), 국제차세대융합기술학회[DOI]
- S. Saha, "Progress in Brain Computer Interface : Challenges and Opportunities", Frontiers in Systems Neuroscience, 15, pp.578875~ (2021)
- D. S. Bolme, "Visual Object Tracking Using Adaptive Correlation Filters", IEEE Computer Society Conference on Computer Vision and Pattern Recognit, pp.2544~2550 (2010)
- 윤종섭, "시선 추적 센서 데이터를 활용한 뇌파 잡파제거 방법에 관한 연구", 전기전자학회논문지, 22(4), pp.1109~1114 (2018), 한국전기전자학회[DOI]
- 장준서, "다중 이벤트 센서 기반 스마트 홈에서 사람 행동 분류를 위한 효율적 의사결정평면 생성기법", 대한임베디드공학회논문지, 14(5), pp.277~286 (2019), 대한임베디드공학회[DOI]
- 정희철, "대용량 이미지넷 인식을 위한 CNN 기반 Weighted 앙상블 기법", 대한임베디드공학회논문지, 15(4), pp.197~204 (2020), 대한임베디드공학회[DOI]
- D. Sabbagh, "Predictive Regression Modeling with MEG", EEG: from Source Power to Signals and Cognitive States, 2020, pp.222, ~
- C. Pan, "EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands", Jour. Applied Sciences, 10(5) (2020)
- A. X. Stewart, "Single-trial Classification of EEG in a Visual Object Task Using ICA and Machine Learning", Journal of Neuroscience Methods, 228, pp.1~14 (2014)