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Practical Online Brain-Computer Interface System Based on Motion-onset Visual Responses

Author: LiuTao
Tutor: HongBo
School: Tsinghua University
Course: Biomedical Engineering
Keywords: Movement starting visual evoked potential Brain - Computer Interface Adaptive Algorithm Support Vector Machine Independent Component Analysis
CLC: R318.0
Type: Master's thesis
Year: 2010
Downloads: 93
Quote: 0
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Abstract


Brain - computer interface technology is newly developed, multi-channel a new form of human-computer interaction. With the previous forms of human-computer interaction, the brain - computer interface does not depend on the person's normal output channels (peripheral nerve and muscle tissue, etc.), the human brain directly for information exchange and the external environment. Although the concept of the brain - computer interface in a very long been realized in the process towards practical, however there are many problems to be solved. Such as the complexity of the system configuration, the self-adaptability of the online system, the interface is designed to meet the demand for day-to-day environment, are practical issues to be considered. Movement starting visual evoked potentials (motion onset visual evoked potential, mVEP) the first time in recent years the laboratory introduced into the brain - computer interface research. On this basis, this paper systematically studied physiological characteristics of mVEP, such as refractory effect of stimulation mode and contrast on the brain - computer interface system based on mVEP, provides the basis for the system design. In addition, for the N200 component in the stability under different contrast lead options, making mVEP-based brain - computer interface to obtain stable performance in low contrast. EEG feature extraction and pattern classification is the core of the brain - computer interface. Made improvements on the existing classification support vector machine (SVM) to enable it to better achieve the brain - computer interface unbalanced sample classification. This article also raised expectations and judgment based on two adaptive decision-making criteria the judgment accuracy rate expectations, effectively improving the performance of brain - computer interface. In this paper, based on a class SVM asynchronous brain - computer interface algorithms, and through the promotion of the posterior probability model, the \Research and algorithm based on the paper, we have developed the first online mVEP-based single-lead brain - machine interface. 12 subjects to use the system on the average information transmission rate of 42.1 bits / min. In addition, we developed on the basis of a set of Web browsing and search system. All 12 subjects in the experiment are able to achieve ease of operation of the system, and to prove that the system can be applied to the actual scene, the embedding and adapt to the type of interface as a brain-machine interactive ultimately achieve high efficiency. Finally, we use the independent component analysis-depth analysis of selective attention mVEP ingredients, and verify the feasibility of the based on independent mVEP brain - computer interface.

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CLC: > Medicine, health > Basic Medical > Medical science in general > Biomedical Engineering > General issues
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