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Motor imagery (MI)-based brain-machine interface (BMI) is a technology under development that actively modifies users’ perception and cognition through mental tasks, so as to decode their intentions from their neural oscillations, and thereby bringing some kind of activation. So far, MI as control task in BMIs has been seen as a skill that must be acquired, but neither user conditions nor controlled learning conditions have been taken into account. As motor system is a complex mechanism trained along lifetime, and MI-based BMI attempts to decode motor intentions from neural oscillations in order to put a device into action, motor mechanisms should be considered when prototyping BMI systems. It is hypothesized that the best way to acquire MI skills is following the same rules humans obey to move around the world. On this basis, new training paradigms consisting of ecological environments, identification of control tasks according to the ecological environment, transparent mapping, and multisensory feedback are proposed in this chapter. These new MI training paradigms take advantages of previous knowledge of users and facilitate the generation of mental image due to the automatic development of sensory predictions and motor behavior patterns in the brain. Furthermore, the effectuation of MI as an actual movement would make users feel that their mental images are being executed, and the resulting sensory feedback may allow forward model readjusting the imaginary movement in course.

Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery