Noninvasive measurement method, such as EEG, fMRI, or NIRS, has been used for brain-computer interface. EEG has nice temporal resolution, and it is used for BCI, such as amplitudes of different frequency bands
imagining movement of different parts of the body
slow cortical potentials and gamma-band rhythms. Recently, electrocorticography (ECoG) is an alternative approach to less invasive BMIs. Since ECoG records directly from neuronal activities on the cortical surface, ECoG has higher spatiotemporal resolution with better signal-to-noise ratio than scalp EEG. ECoG has also shown potential as a stable long-term recording method. Several studies using ECoG have already succeeded in the classification of movement direction, grasp type, and prediction of hand trajectory. In this chapter, we introduce a new motor control hypothesis using a simple mathematical musculoskeletal model. Then we introduce reconstruction of muscle activity time series by computational model and discuss the motor control and also BMI using ECoG recordings.