论文标题
使用广义的极值分布在运动图像脑图像信号中的MU抑制检测
Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution
论文作者
论文摘要
本文介绍了脑部计算机界面（BCI）中脑电图（EEG）信号的MU抑制检测。为此，根据统计模型和线性分类器提出了有效的算法。确切地说，提出了广义的极值分布（GEV），以表示中央运动皮层中脑电图信号的功率谱密度。使用最大似然法估算相关的三个参数。基于这些参数，简单有效的线性分类器旨在对三种类型的事件进行分类：图像，运动和休息。初步结果表明，可以使用非常好的分类精度来准确检测提出的统计模型以精确检测MU抑制并区分不同的EEG事件。
This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very good classification accuracy.
