  With the recent developments in neuroscience and engineering, it is now
possible to record brain signals and decode them. Also, a growing number of
stimulation methods have emerged to modulate and influence brain activity.
Current brain-computer interface (BCI) technology is mainly on therapeutic
outcomes, it already demonstrated its efficiency as assistive and
rehabilitative technology for patients with severe motor impairments. Recently,
artificial intelligence (AI) and machine learning (ML) technologies have been
used to decode brain signals. Beyond this progress, combining AI with advanced
BCIs in the form of implantable neurotechnologies grants new possibilities for
the diagnosis, prediction, and treatment of neurological and psychiatric
disorders. In this context, we envision the development of closed loop,
intelligent, low-power, and miniaturized neural interfaces that will use brain
inspired AI techniques with neuromorphic hardware to process the data from the
brain. This will be referred to as Brain Inspired Brain Computer Interfaces
(BI-BCIs). Such neural interfaces would offer access to deeper brain regions
and better understanding for brain's functions and working mechanism, which
improves BCIs operative stability and system's efficiency. On one hand, brain
inspired AI algorithms represented by spiking neural networks (SNNs) would be
used to interpret the multimodal neural signals in the BCI system. On the other
hand, due to the ability of SNNs to capture rich dynamics of biological neurons
and to represent and integrate different information dimensions such as time,
frequency, and phase, it would be used to model and encode complex information
processing in the brain and to provide feedback to the users. This paper
provides an overview of the different methods to interface with the brain,
presents future applications and discusses the merger of AI and BCIs.
