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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.10 P.805-806


Brain-machine interface (BMI) and cyborg intelligence

Author(s):  Zhao-hui Wu

Affiliation(s):  Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   wzh@zju.edu.cn

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Zhao-hui Wu. Brain-machine interface (BMI) and cyborg intelligence[J]. Journal of Zhejiang University Science C, 2014, 15(10): 805-806.

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Brain-machine interfaces (BMIs) aim at building a direct communication pathway between the brain and an external device, and represent an area of research where significant progress has been made during the past decade. Based on BMIs, mind information can be read out by neural signals to control machine actuators, and machine-coded sensory information can be delivered to specific areas in the brain. BMI techniques provide the opportunity to integrate machine intelligence with biological intelligence at multiple levels to develop a more powerful intelligent system, thereby creating a new field called ‘cyborg intelligence’.       Traditional artificial intelligence (AI) involves learning how to emulate human-like intelligence and the creation of machines or software systems that exhibit intelligence. Successful AI includes natural language processing, speech recognition, smart search engines, face recognition, and Q&A systems. Although AI techniques show advantages in high-performance computation, probabilistic models, statistical reasoning, optimization, and almost unlimited storage, modern AI systems are unlikely to match humans in learning, high-level reasoning, and flexible adaptation to varying environments, which are recognized as the best features of biological intelligence. Because of their obvious complementary strengths, studies of the convergence of machine and biological intelligence, i.e., cyborg intelligence, are of great significance in maximizing their capabilities through their integration.       The central problems of cyborg intelligence include information fusion and representation in sensory-motor integration, cognitive computational models in brain-machine collaborations, statistical models for decoding and encoding brain signals, computational models and architecture for cyborg intelligence, and related data and computation standards. To explore this exciting new field, two workshops were held in Changsha and Hangzhou, China, in October and December 2013, respectively. More than 30 experts from the neuroscience, neuroengineering, and computer science fields came together to discuss scientific problems and research trends in the cyborg intelligence area. This special issue includes five of the most interesting and significant presentations describing state-of-the-art research in this field.

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[2]Qi, Y., Ma, F.Q., Ge, T.T., et al., 2014. A bidirectional brain computer interface for effective epilepsy control. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(10): 839-847.

[3]Wang, H.T., Li, Y.Q., Yu, T.Y., 2014. Coordinated control of an intelligent wheelchair based on a brain-computer interface and speech recognition. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(10):832-838.

[4]Wu, D., Li, C.Y., Liu, J., et al., 2014. Scale-free brain ensemble modulated by phase synchronization. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(10):821-831.

[5]Zhou, H., Yang, L., Wu, F.X., et al., 2014. Exploring the mechanism of neural-function reconstruction by reinnervated nerves in targeted muscles. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(10):813-820.

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