A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery

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 NIBS 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 NIBS system. Conclusions: From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI. (C) 2014 Elsevier B.V. All rights reserved.