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

Abstract

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.

A review of user training methods in brain computer interfaces based on mental tasks

“…To do so, a first idea is to gradually confront the user with the BCI system and intended application. As an example, the study in [278] described distinct exercises, with a first look at brain signal modulation using MI tasks and classical BCI feedback [22], followed by an explanation of the functional role of the MI task and then a transfer task with real online control. Another more extensive example would be the study in [279] in which users were gradually guided towards a control task with different protocol components, including VR interactions and robotic control.…”

Interfaces Cerebro-Computadora: Conceptualización, Retos de Rediseño e Impacto Social

“…Esta incongruencia entre tarea mental y comando de control podría ocasionar una alteración en el sentido de autonomía que afectaría el desempeño del usuario al interactuar con la ICC. La congruencia se logra, entonces, cuando el ambiente de una ICC se modifica como si los movimientos imaginarios estuvieran siendo realmente ejecutados [ 47] . Ejemplo de ello es el estudio de [48], donde el movimiento imaginado por los participantes (movimiento del brazo derecho) correspondió con la acción ejecutada por el sistema (movimiento de un brazo en pantalla y estimulación háptica del usuario) al detectar el estado de imaginación.…”