TY - GEN
T1 - A support vector brain-machine interface for cortical control of directions
AU - Hu, Jing
AU - Si, Jennie
AU - Olson, Byron
AU - He, Jiping
PY - 2006/12/22
Y1 - 2006/12/22
N2 - A closed-loop brain-machine interface system (BMI) was implemented using freely-moving rats. Instead of reproducing continuous natural limb movements as in many other BMI work, abstract supervisory control commands such as Go left, Go right, were extracted from neurons in the motor and premotor areas of the rats brain. The control output was thus formulated as a solution of a nonlinear support vector machine (SVM). Five male Sprague-Dawley rats were able to use such a BMI. Furthermore, evidence was found that the animal changed his behavior and neural activity during the use of the interface from the hand-control phase to the brain-control phase. The analysis showed that the animal adapted a subset of its neural activities to make his decisions more distinct in the SVM decision space from neural activities, which subsequently led to improved brain-control task performance. Two independent approaches, an SVM model sensitivity analysis and a model-free mutual information analysis, pointed to the same subset of neurons that were responsible for such changes.
AB - A closed-loop brain-machine interface system (BMI) was implemented using freely-moving rats. Instead of reproducing continuous natural limb movements as in many other BMI work, abstract supervisory control commands such as Go left, Go right, were extracted from neurons in the motor and premotor areas of the rats brain. The control output was thus formulated as a solution of a nonlinear support vector machine (SVM). Five male Sprague-Dawley rats were able to use such a BMI. Furthermore, evidence was found that the animal changed his behavior and neural activity during the use of the interface from the hand-control phase to the brain-control phase. The analysis showed that the animal adapted a subset of its neural activities to make his decisions more distinct in the SVM decision space from neural activities, which subsequently led to improved brain-control task performance. Two independent approaches, an SVM model sensitivity analysis and a model-free mutual information analysis, pointed to the same subset of neurons that were responsible for such changes.
KW - Brain-machine interfaces
KW - Motor learning
KW - Neural interfaces
KW - Neuro-robotics
KW - Neuron adaptation
UR - http://www.scopus.com/inward/record.url?scp=33845581430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845581430&partnerID=8YFLogxK
U2 - 10.1109/BIOROB.2006.1639204
DO - 10.1109/BIOROB.2006.1639204
M3 - Conference contribution
AN - SCOPUS:33845581430
SN - 1424400406
SN - 9781424400409
T3 - Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
SP - 893
EP - 898
BT - Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
T2 - 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006
Y2 - 20 February 2006 through 22 February 2006
ER -