It takes about a day to use the data and establish a model used for estimating the movements of the user's fingertip. However, after the learning process, the system can estimate the movements at high speeds.
This time, the researchers operated the system as an offline system, but they are now researching on a system that operates in real time. In that case, the delay time is about 0.5 seconds. Most of the delay time is the time it takes for an enormous quantity of the MEG's time-series measurement data to be transferred among multiple computers.
Estimation conducted in 2 steps; sparse logistic regression used in latter part
The estimation of the movements of a fingertip is conducted in two steps. First, based on the data collected by the MEG, the current is estimated at 1,500 current sources that are virtually and evenly arranged on the surface of the cerebral cortex. Then, a dimensionality reduction technique called "sparse estimation (SLR: sparse logistic regression)" is used to extract only the current sources that are related to the movements of the fingertip.
As a result, 200 current sources, including pyramidal area, parietal association area and somatosensory area, that are related to the movements are automatically chosen. The learning process for establishing an SLR model takes several hours to complete. The SLR is a dimensionality reduction technique developed by ATR, and it is also used for the BMI technology for the Asimo.
The MEG is equipped with 400 channels of magnetic field sensors, but each sensor is affected by mingling magnetic fields generated in various regions of the brain. The number of the channels used for the measurement (400) is smaller than the number of unknown current sources (1,500). So, it is a so-called ill-posed problem.
To solve the problem, the researchers utilized the data collected by the fMRI. Specifically, an "inverse filter" is established based on the fMRI's data for restoring 1,500 current sources in the brain by using the MEG's data. To establish the filter, they used an fMRI having a high spatial resolution. And, when the BMI is actually used, an MEG that can output data at a speed as high as 1kHz is used. For the learning of the inverse filter, the "variational Bayes approach" was employed.
Removing influence of electric fields generated by myoelectric signals
An MEG is a device that measures the weak electric fields generated by the spike electricity of brain neurons. Therefore, it also measures the electric fields generated by myoelectric signals outside the brain as in the case of an EEG. For example, they are electric fields generated by heartbeat and eye movements.
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