Methods for Improving Brain-Computer Interface: Using A Brain-Directed Adjuvant and A Second-Generation Artificial Intelligence System to Enhance Information Streaming and Effectiveness of Stimuli

ABSTRACT

Background: The brain-computer interface (BCI) is gaining much attention to treat neurological disorders and improve brain-dependent functions. Significant achievements over the last decade have focused on engineering and computation technology to enhance the recording of signals and the generation of output stimuli. Nevertheless, many challenges remain for the translation of BCIs to clinical applications. Methods: We review the relevant data on the four significant gaps in enhancing BCI's clinical implementation and effectiveness. Results: The paper describes three methods to bridge the current gaps in the clinical application of BCI. The first is using a brain-directed adjuvant with a high safety profile, which can improve the accuracy of brain signaling, summing of information, and production of stimuli. The second is implementing a second-generation artificial intelligence system that is outcome-oriented for improving data streaming, recording individualized brain-variability patterns into the algorithm, and improving closed-loop learning at the level of the brain and with the target organ. The system overcomes the compensatory mechanisms that underlie the loss of stimuli' effectiveness for ensuring sustainable effects. Finally, we use inherent brain parameters relevant to consciousness and brain function to bridge some of the described gaps. Conclusions: Combined with the currently developed techniques for enhancing effectiveness and ensuring a sustainable response, these methods can potentially improve the clinical outcome of BCI techniques.