Exploring the Visual Guidance of Motor Imagery in Sustainable Brain-Computer Interfaces

Motor imagery brain-computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user's performance impacts the system's overall accuracy, and concentrating on the user's mental load enables a better evaluation of the system's overall performance. The impacts of various levels of abstraction on visual guidance of mental training in motor imagery (MI) may be comprehended. We proposed hypotheses about the effects of visually guided abstraction on brain activity, mental load, and MI-BCI performance, then used the event-related desynchronization (ERD) value to measure the user's brain activity, extracted the brain power spectral density (PSD) to measure the brain load, and finally classified the left- and right-handed MI through a support vector machine (SVM) classifier. The results showed that visual guidance with a low level of abstraction could help users to achieve the highest brain activity and the lowest mental load, and the highest accuracy rate of MI classification was 97.14%. The findings imply that to improve brain-computer interaction and enable those less capable to regain their mobility, visual guidance with a low level of abstraction should be employed when training brain-computer interface users. We anticipate that the results of this study will have considerable implications for human-computer interaction research in BCI.

Automated summary

Motor imagery brain-computer interface (MI-BCI) systems have the potential to restore motor function and enable individuals with motor and sensory impairments to live independently. This study explores the effects of visual guidance with different levels of abstraction on brain activity, mental load, and MI-BCI performance. The results show that low-level abstraction visual guidance can enhance brain activity and reduce mental load, leading to improved accuracy in MI classification.