Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

• Main Authors: He, F. (Author), Qi, H. (Author), Tao, X. (Author), Zhou, L. (Author), Zhou, P. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: adult; Article; artificial neural network; Brain; brain computer interface; Brain computer interface; brain depth stimulation; Brain-computer interface; Brain-Computer Interfaces; cerebrovascular accident; clinical article; clinical trial; dorsolateral prefrontal cortex; electroencephalography; Electroencephalography; electrostimulation; Event related desynchronization; Evoked Potentials, Somatosensory; false triggering; False triggering; feasibility study; female; hand; Hand; human; Humans; imagination; Imagination; machine learning; male; measurement accuracy; Mental activity; mental performance; motor activity; motor cortex; motor evoked potential; motor imagery; neurorehabilitation; Post-stroke rehabilitation; receiver operating characteristic; Recognition models; Rehabilitation training; sensitivity and specificity; signal noise ratio; somatosensory evoked potential; spinal cord injury; steady-state somatosensory evoked potential; Steady-state somatosensory evoked potential (SSSEP); stroke rehabilitation; support vector machine; Target and non targets; task specificity; visual stimulation
• Online Access: View Fulltext in Publisher

 In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p < 0.05), but the sensitivity was not significantly different. These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training. © 2013 IEEE.