Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints

Understand

The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs).

• Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature.
• The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent.
• Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs.