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Common spatial pattern method for real-time eye state identification by using electroencephalogram signals

机译:利用脑电图信号实时识别眼睛状态的通用空间模式方法

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摘要

Cross-channel maximum and minimum are used to monitor real-time electroencephalogram signals in 14 channels. On detection of a possible change, multivariate empirical mode decomposed the last 2 s of the signal into narrow-band intrinsic mode functions. Common spatial pattern is then utilised to create discriminating features for classification purpose. Logistic regression, artificial neural network, and support vector machine classifiers all could detect the eye state change with 83.4% accuracy in <;2 s. This algorithm provides a valuable improvement in comparison with a recent procedure that took about 20 min to classify new instances with 97.3% accuracy. Application of the introduced algorithm in the real-time eye state classification is promising. Increasing the training examples could even improve the accuracy of the classification analytics.
机译:跨通道的最大值和最小值用于监视14个通道中的实时脑电图信号。在检测到可能的变化时,多元经验模式将信号的最后2 s分解为窄带固有模式函数。然后,利用公共空间模式来创建用于分类目的的区分特征。 Logistic回归,人工神经网络和支持向量机分类器都可以在<; 2 s内以83.4%的精度检测眼睛状态变化。与最近的过程相比,该算法提供了宝贵的改进,该过程花费了大约20分钟的时间对新实例进行分类,准确度为97.3%。引入的算法在实时眼睛状态分类中的应用前景广阔。增加训练示例甚至可以提高分类分析的准确性。

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