gms | German Medical Science

64th Annual Meeting of the German Society of Neurosurgery (DGNC)

German Society of Neurosurgery (DGNC)

26 - 29 May 2013, Düsseldorf

Brain machine interface for motor function reconstruction

Meeting Abstract

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  • Jianmin Zhang - Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine; Qiushi Academy for Advanced Studies, Zhejiang University

Deutsche Gesellschaft für Neurochirurgie. 64. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC). Düsseldorf, 26.-29.05.2013. Düsseldorf: German Medical Science GMS Publishing House; 2013. DocMO.18.05

doi: 10.3205/13dgnc158, urn:nbn:de:0183-13dgnc1580

Published: May 21, 2013

© 2013 Zhang.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



Brain Machine Interface (BMI) can establish an information pathway between brain and the external devices. By the means of BMI technology, limb paralysis or disabilities can take advantage of electronic equipment (e.g., mechanical arm, wheel chair, etc) to replace their lost limbs, realizing the reconstruction of movement function. Besides, the research on BMI technology plays a profound scientific role in understanding the mechanism of brain and the nerve disease. Moreover, when taking national security issues, the development of treatment on mental diseases and rehabilitation medical device into consideration, BMI has an important meaning to the whole society with many promising applications. BMI has drawn great attentions from the international academia, and it is now the cutting edge issues in the field of neural science, clinic medical and information engineering. In order to apply BMI technology into the rehabilitation of movement function, this project will build an interface system of non-human primates. Two 96-channel Utah arrays were implanted in monkey's primary motor cortex and premotor cortex dorsal (pmd) to record the neural signals. By using generalized neural network and other pattern recognition algorithms to analyze and decode the monkey wrist trajectory when it is manipulating the joystick. Results showed that high-quality spike signal could be chronically obtained from M1 or PMd area of monkey. Spikes from M1 could be used precisely to predict the direction and trajectory of monkey wrist. As for graspgesture, there are corresponding correlation between most neurons of PMd and different gestures. Off-line analysis results showed that different gestures could be decoded correctly by neural signals, whose accuracy is higher than 95%. Meanwhile, we achieved on-line control by brain signals. Money can control the robot arm to finish four different gestures with 85% accuracy.

Brain Machine Interfaces (BMI) had been an important subset of neurosciences and neuro-engineering along with the rapidly development of brain machine interface techniques and success of early clinic and experimental study. Nowadays, BMI is widely used in reconstruction of sensation and motion and treatment of neuropsychiatric disorders. In the future, application of BMI technology will be penetrated into all aspects in daily life, especially to enhance and expand normal human physiological function. However, BMI is still in early stages, and all technical issues need to be further improved. Multi-disciplinary researchers should work together to push the development of BMI techniques.