Mental rehearsal has long been known by psychologists and behavioral scientists to better prepare the mind for performing a future task. Visualizing yourself becoming lucid in dreams is a core skill of the Mnemonic Induction of Lucid Dreaming (MILD) technique. MILD integrates several complex skills to best help you carry your pre-set intentions into your sleep and dreams, and execute them. The MILD technique, developed by Stephen LaBerge in the late 70s, has been shown to be effective in increasing how often you can visit the lucid dream world, but many questions remain as to how MILD skills can be enhanced to make entry to the lucid dream state more accessible.
Modern technology has great potential for enhancing access to the lucid dream state. The Dreamlight lucid dreaming mask (and its series of successors by LaBerge) is one example of a device that used mind-body technology to produce more lucid dreams. The device flashes light cues into your eyes during dreams which are perceived, and ideally recognized, as a dreamsign. This is a technology that has great potential for continued, multidisciplinary development. But how can developing technologies be used to enhance other core components of MILD, such as the imaginary rehearsal of recognizing dreamsigns?
A Brain-Computer-Interface (BCI) involves a feedback loop between the brain's electrical activity and an external stimulus to promote learning. Basic science research of mental rehearsal recently used BCI with monkeys as a lens for studying how mentally rehearsing an action better prepared the monkey to physically perform the skill in the real world.
An innovative BCI application would be to use virtual simulations of nonlucid dreams to mentally rehearse the act of recognizing dreamsigns. This, coupled with neurofeedback training to match the neurophysiological signatures of achieving lucidity in direct correspondence with the perceived BCI imagery, could further promote learning in theory. Taken further, integrating VR and AR into BCI systems could take this type of learning to a level that is even more sophisticated.
In the study, the monkey was also able to generalize the same alteration in brainwaves to take action with a similar stimulus, which is directly relevant to dreamsign recognition training since dreamsigns have many variations.
Of course, in the case of BCI-assisted lucid dreaming, science has a long way to go before it accurately captures the EEG signatures that best mimic the brain events for becoming lucid, and integrates this into a BCI system. Nevertheless, the possibilities for technology-assisted lucid dreaming are quite captivating...almost as captivating as the lucid dreaming state in itself.