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首页> 外文会议>2010 3rd International Conference on Biomedical Engineering and Informatics >Application of quadratic optimization to multi-class common spatial pattern algorithm in brain-computer interfaces
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Application of quadratic optimization to multi-class common spatial pattern algorithm in brain-computer interfaces

机译：二次优化在脑机接口多类通用空间模式算法中的应用

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Common spatial pattern (CSP) algorithm is a highly successful method for the motor imagery based brain-computer interfaces (BCIs) in the case of two task conditions. But low information transfer rate (ITR) is an intrinsic problem that binary BCIs face, and restricts their practical application. The most effective method to increase ITR is to extend two mental tasks to multiple tasks. This paper generalizes binary CSP algorithm to multiple task conditions by approximate joint diagonalization based on quadratic optimization. This algorithm is used to five data sets recorded during a BCI experiment consisting of three motor imagery tasks and is evaluated by diagonalization error, convergence speed and classification accuracy. Results demonstrate that the performance of the algorithm is satisfactory.
机译：对于两个任务条件下的基于运动图像的脑机接口（BCI），通用空间模式（CSP）算法是一种非常成功的方法。但是，较低的信息传输速率（ITR）是二进制BCI所面临的固有问题，并限制了其实际应用。增加ITR的最有效方法是将两个心理任务扩展到多个任务。通过基于二次优化的近似联合对角化，将二进制CSP算法推广到多个任务条件。该算法用于BCI实验期间记录的五个数据集，该数据集由三个运动图像任务组成，并通过对角化误差，收敛速度和分类准确性进行评估。结果表明，该算法的性能令人满意。

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