title: High-accuracy brain-machine interfaces using feedback information.
abstract: Sensory feedback is very important for movement control. However, feedback information has not been directly used to update movement prediction model in the previous BMI studies, although the closed-loop BMI system provides the visual feedback to users. Here, we propose a BMI framework combining image processing as the feedback information with a novel prediction method. The feedback-prediction algorithm (FPA) generates feedback information from the positions of objects and modifies movement prediction according to the information. The FPA predicts a target among objects based on the movement direction predicted from the neural activity. After the target selection, the FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target. The FPA repeats the modification in every prediction time points. To evaluate the improvements of prediction accuracy provided by the feedback, we compared the prediction performances with feedback (FPA) and without feedback. We demonstrated that accuracy of movement prediction can be considerably improved by the FPA combining feedback information. The accuracy of the movement prediction was significantly improved for all subjects (P<0.001) and 32.1% of the mean error was reduced. The BMI performance will be improved by combining feedback information and it will promote the development of a practical BMI system.
authors: Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Republic of Korea; MEG center, Seoul National University Hospital, Seoul, Republic of Korea.
authors: Hong Gi
authors: HG
authors: Yeom
authors: MEG center, Seoul National University Hospital, Seoul, Republic of Korea; Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea; Sensory Organ Research Institute, Seoul National University, Seoul, Republic of Korea.
authors: June Sic
authors: JS
authors: Kim
authors: Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Republic of Korea; MEG center, Seoul National University Hospital, Seoul, Republic of Korea; Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea; Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
authors: Chun Kee
authors: CK
authors: Chung
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D062207:Brain-Computer Interfaces; D005246:Feedback; D006801:Humans; D012815:Signal Processing, Computer-Assisted