  The mission of visual brain-computer interfaces (BCIs) is to enhance
information transfer rate (ITR) to reach high speed towards real-life
communication. Despite notable progress, noninvasive visual BCIs have
encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are
achievable. In this study, we investigate the information rate limits of the
primary visual channel to explore whether we can and how we should build visual
BCI with higher information rate. Using information theory, we estimate a
maximum achievable ITR of approximately 63 bits per second (bps) with a
uniformly-distributed White Noise (WN) stimulus. Based on this discovery, we
propose a broadband WN BCI approach that expands the utilization of stimulus
bandwidth, in contrast to the current state-of-the-art visual BCI methods based
on steady-state visual evoked potentials (SSVEPs). Through experimental
validation, our broadband BCI outperforms the SSVEP BCI by an impressive margin
of 7 bps, setting a new record of 50 bps. This achievement demonstrates the
possibility of decoding 40 classes of noninvasive neural responses within a
short duration of only 0.1 seconds. The information-theoretical framework
introduced in this study provides valuable insights applicable to all
sensory-evoked BCIs, making a significant step towards the development of
next-generation human-machine interaction systems.
