A brain-machine interface (BMI) is an interface technology that uses neurophysiological signals from the brain to control external machines. Recent invasive BMI technologies have succeeded in the asynchronous control of robot arms for a useful series of actions, such as reaching and grasping. In this study, we developed non-invasive BMI technologies aiming to make such useful movements using the subject's own hands by preparing a BMI-based occupational therapy assist suit (BOTAS). We prepared a pre-recorded series of useful actions—a grasping-a-ball movement and a carrying-the-ball movement—and added asynchronous control using steady-state visual evoked potential (SSVEP) signals. A SSVEP signal was used to trigger the grasping-a-ball movement and another SSVEP signal was used to trigger the carrying-the-ball movement. A support vector machine was used to classify EEG signals recorded from the visual cortex (Oz) in real time. Untrained, able-bodied participants (n = 12) operated the system successfully. Classification accuracy and time required for SSVEP detection were ~88% and 3 s, respectively. We further recruited three patients with upper cervical spinal cord injuries (SCIs); they also succeeded in operating the system without training. These data suggest that our BOTAS system is potentially useful in terms of rehabilitation of patients with upper limb disabilities.
A predictive behavior called "negative asynchrony" is well known in the sensory-motor coupling. This phenomenon means motion timings precede the cyclic onset of stimuli, and it is commonly observed in the synchronization tapping task. With the use of dual-task method, Miyake et al. had already analyzed this phenomenon and reported two types of anticipatory timing control. However, in such previous researches, the tapping task has been investigated by statistical analysis of synchronization errors (SE). In this report, we assume that the asynchronous displacement has temporal structure. And time-series analysis was applied to clarify it in frequency response. As a consequence, it was shown that anticipatory behavior in the tapping task had two different characters corresponding to two types of anticipatory timing control. One is characterized by a 1/fn power-law relation between spectral power and frequency. The other is characterized by the combination of the significant peak of pacing stimuli and a white noise.
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