Decoding Motor Imagery of three-dimensional Random Movements Using Electrocorticographic Signals in Acute Training Setting

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Abstract Background: Decoding movement-related signals from widespread brain areas may be advantageous given that diverse brain areas are activated for complex movements and imagined movements. Electrocorticography (ECoG) is a beneficial option in that it covers wide brain areas with a high signal-to-noise ratio. Here, we used ECoG to decode human reach-and-grasp movements and imagined movements. Method: Twenty-two epileptic patients with intracranial electrodes were asked to execute and imagine reach-and-grasp movements toward random targets with training periods of 8.2± 3.4 days. A multiple linear regression algorithm was used to estimate the offline prediction of the movement and imagined movement trajectories.Result: The three-dimensional reaching trajectories were decoded in motor execution (ME) and motor imagery (MI) tasks. The mean correlation coefficients for the x-axis, y-axis, and z-axis were correspondingly 0.5, 0.53, and 0.51 for the ME and 0.37, 0.19, and 0.28 for the MI. Conclusion: MI could be decoded in subjects who were acutely trained with acceptable performance when given a random target presentation task. ECoG brain-machine interface (BMI) given its wide coverage may be practical for decoding movements with multiple degrees of freedom in humans.

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