Learning of motor control using simulated brain base on biological signals

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(1) We consider a possibility for estimating EMG and force amplitude based on hemoglobin density. In experiments, subjects carried out isometric movements of three levels of force amplitude in order to measure EMG, force amplitude and hemoglobin density, and these relationships were investigated. We confirmed strong correlations between these measurements. From these relationships we propose two estimation models; one is to estimate the EMG from hemoglobin density and the other is to estimate the force amplitude from the estimated EMG. Finally, we show an example of a BMI system applying estimation models to control an arm robot.(2) This research proposes a method to classify near-infrared spectroscopy (NIRS) signals responded by left-hand or right-hand motor execution. In the proposed method, we selected features as sampling intervals and measured channels from NIRS signals. Support vector machines were used as a classifier. It is shown that the proposed method presents the high generalization capability. The result suggests that the proposed method can be useful to classify the two-category classification problem of NIRS signals.(3) We applied event-related potential (ERP) to reinforcement signals that are equivalent to reward and punishment signals. We conducted an EEG in which volunteers identified the success or failure of a task. We confirmed that there were differences in the EEG depending on whether the task was successful or not and suggested that ERP might be used as a reward of reinforcement leaning. We used a SVM for recognizing the P300. We selected the feature vector in SVM that was composed of averages of each 50 ms for each of the six channels for a total of 700 ms. We can suggest that reinforcement learning using P300 can be performed accurately.