Abstract
Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user’s intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.
Highlights
Volitional movement prediction has become an area of focus of brain-machine interface (BMI) research
This study clearly shows the movement intention can be successfully predicted by connectivity dynamics which may reflect changes in movement states
Network dynamics needs to be directly compared to conventional models based on spectral power analysis to determine whether functional connectivity is better, worse, or no different
Summary
Volitional movement prediction has become an area of focus of brain-machine interface (BMI) research. Three-dimensional movements have been successfully controlled by BCI models using the primary motor area activities in humans and primates [1,2,3]. The prediction models implicated the motor parameters of hand position, velocity, and force as crucial during movement. The conventional experimental paradigm for motor control consists of the active. Prediction of movement intention using brain connectivity collection and analysis, decision to publish, or preparation of the manuscript
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