  The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer
Interfaces (BCIs) allow the direct communication between humans and machines by
exploiting the neural pathways connected to motor imagination. Therefore, these
systems open the possibility of developing applications that could span from
the medical field to the entertainment industry. In this context, Artificial
Intelligence (AI) approaches become of fundamental importance especially when
wanting to provide a correct and coherent feedback to BCI users. Moreover,
publicly available datasets in the field of MI EEG-based BCIs have been widely
exploited to test new techniques from the AI domain. In this work, AI
approaches applied to datasets collected in different years and with different
devices but with coherent experimental paradigms are investigated with the aim
of providing a concise yet sufficiently comprehensive survey on the evolution
and influence of AI techniques on MI EEG-based BCI data.
