| dc.description.abstract | Brain Computer Interface (BCI) provides a way for people with nerve or muscle damage to communicate with the outside world using brain waves. In the past, subjects were required to perform motor imagery. However, it is difficult for paralyzed patients to do motor imagery. Therefore, we used the human Mirror Neuron System (hMNS) as a method to train motor imagery, combined with VR (virtual reality) to watch videos establish an imaginary basis, respectively establish an individual model and a global model, and then ask the subjects to recall the VR scene to perform motor imagery, and finally establish a real-time feedback brain-computer interface for retrospective imagination, so that the motor imagery training process is simpler and more convenient.
The experiment is divided into three parts, the first part is to establish a mirror nerve action observation brain wave model, the second part is to establish a retrospective imaginative transfer learning model, and the third part uses the previously trained model to establish a BCI real-time feedback system. In the experiment, the dry brainwave electrodes are set at the positions of F3, F4, C3, Cz, C4, P3, Pz, and P4 of the 10-20 EEG System. The model requires two seconds of brainwave data, and the sliding window moves every 0.1 seconds, using action observation, retrospective imagination, and real-time feedback to strengthen the model to make real-time BCI judgments.
There were five subjects in the experiment, all of them were healthy males between the ages of 20 and 22, and all of them were right-handed. The results show that on the idividual model, the average accuracy of the observation model is 55.8%, the average accuracy of retrospective imagination is 63.6%, and the average accuracy of instant feedback is 73.1%. On the global model, the average accuracy of the observation model is 50%, the average accuracy of retrospective imagination is 61%, and the average accuracy of instant feedback is 66.5%. | en_US |