Development of Electrical Neural Stimulator Generating Periodic and Non-periodic Signals and Supporting Closed-loop Experimental System

It is essential to build a system to generate proper neural stimulus signals with adjusting parameters. We developed a stimulator with up to four channels for separate settings in periodic and non-periodic modes. The device can support a closed-loop experimental system which utilizes neural information in real time as a feedback for controlling stimuli. To confirm whether stimulating signals are properly produced and delivered inside the brain, two experiments with rats were conducted. We observed that the change of firing rates and the cross-power spectral density increased after stimulation which meant that electric signals were transferred well and that they affected the neurons' activities. Thus, it is expected that the stimulator can be utilized to produce appropriate stimulation signals in accordance with various objectives.

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi-finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures.