Bonkon Koo1, Hwan-Gon Lee2, Yunjun Nam3, Hyohyeong Kang4, Chin Su Koh5, Hyung-Cheul Shin6, Seungjin Choi7. 1. School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Republic of Korea. Electronic address: bkkoo@postech.ac.kr. 2. Department of Physical Education, Hallym University, Chuncheon, Republic of Korea. Electronic address: lfgon75@gmail.com. 3. School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Republic of Korea. Electronic address: druid@postech.ac.kr. 4. Department of Computer Science and Engineering, POSTECH, Republic of Korea. Electronic address: paanguin@postech.ac.kr. 5. Department of Physiology, College of Medicine, Hallym University, Chuncheon, Republic of Korea. Electronic address: chris77@hallym.ac.kr. 6. Department of Physiology, College of Medicine, Hallym University, Chuncheon, Republic of Korea. Electronic address: hcshin@hallym.ac.kr. 7. Department of Computer Science and Engineering, POSTECH, Republic of Korea. Electronic address: seungjin@postech.ac.kr.
Abstract
BACKGROUND: For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. NEW METHOD: In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. RESULTS: An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. COMPARISON WITH EXISTING METHOD(S): As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. CONCLUSIONS: From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI.
BACKGROUND: For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. NEW METHOD: In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. RESULTS: An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. COMPARISON WITH EXISTING METHOD(S): As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. CONCLUSIONS: From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI.