Brain-controlled prosthesis manipulation based on scene graph-SSVEP

被引：2
|
作者：
Li R. [1 ]
Zhang X. [1 ,2 ]
Zhang L. [1 ]
Lu Z. [1 ]
机构：
[1] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an
[2] Key Laboratory of Education Ministry for Modern Design & Robot-Bearing System, Xi'an Jiaotong University, Xi'an
来源：
Hsi An Chiao Tung Ta Hsueh | / 1卷 / 115-121期
关键词：
Brain-controlled prosthesis; Scene graph; Visual evoking;
D O I：
10.7652/xjtuxb201701018
中图分类号：
学科分类号：
摘要：
To solve the problems of low recognition rate and stability of traditional steady-state visual evoked potential (SSVEP) methods, a new scene-graph SSVEP approach was proposed. The new scene-graph SSVEP paradigm uses life scenes of normal or disabled persons as the stimulus source. According to the target of prosthesis control, the life scenes are decomposed into corresponding stimulation scene graphs through standardization process of gray scales. After that, a set of contrasting black-and-white reversal scene graphs are obtained for visual stimulation. The new scene-graph SSVEP is evoked by a square pulse modulation with different frequencies and different scene images. Furthermore, the mathematic model of the scene-graph SSVEP nerve conduction is simulated. For recognizing various EEG signals from different scene graph stimulations, canonical correlation analysis (CCA) is used to turn EEG features into control commands. Finally, a brain-controlled prosthesis manipulation platform was built and the new strategy was verified by experiments. Results show that the information transfer rate is approximately 15.34 bit/min, the average accuracy is 91.4% and the highest accuracy is up to 98.44%. It is demonstrated that this method combining normal life scene graphs with traditional SSVEP methods can improved the average recognition accuracy and information transfer rate of prosthesis, and reduce user's visual fatigue. © 2017, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：115 / 121
页数：6
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