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Fig. 4 | Journal of NeuroEngineering and Rehabilitation

Fig. 4

From: Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

Fig. 4

Asynchronous closed-loop control. During this phase, participants engaged in five trials where they utilized their thoughts to control the lower-limb exoskeleton. The experimental setup involved navigating through a pathway that was divided into distinct regions: MOVE areas marked by yellow lines and STOP areas demarcated by red lines. Within the MOVE areas, participants were required to engage in motor imagery of the gait until a command was sent to the exoskeleton, initiating the walking motion. To maintain the gait, participants were instructed to maintain an idle state until they reached the STOP area. Upon entering the STOP area, participants were tasked with performing a single stop. This involved mentally imagining the movement of stopping the gait. Participants were required to sustain this mental task until a command was issued to the device or until they exited the STOP area and reentered a MOVE area. Failure to execute a stop within the designated STOP area constituted an unsuccessful attempt

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