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Fig. 3 | Journal of NeuroEngineering and Rehabilitation

Fig. 3

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

Fig. 3

BMI calibration. It involved the training phase, during which participants completed a total of 14 trials involving specific mental tasks. Half of these trials were conducted under full static conditions (blue), where participants stood still with the exoskeleton, while the other half involved walking assisted by the exoskeleton during the whole trial (orange). The trials conducted under static and motion conditions followed a similar structure. Each trial started with a 15-s period to allow the convergence of the denoising algorithms. Subsequently, an acoustic cue signaled the initiation of the idle state, during which participants were instructed to relax. Following this, another cue indicated the onset of the motor imagery period. Notably, the motor imagery task differed between static and motion trials. In the static trials, participants were instructed to imagine the act of walking, whereas in the motion trials, the task involved imagining the action of stopping the gait. Specifically, this stopping action was defined as bringing the legs together after completing a step

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