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A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery
Bonkon Koo1, Hwan-Gon Lee2, Yunjun Nam1
1School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Republic of Korea.
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.
