Diez Pablo F, Mut Vicente, Laciar Eric, Torres Abel, Avila Enrique
Gabinete de Tecnología Médica (GATEME), Universidad Nacional de San Juan (UNSJ), Argentina.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2579-82. doi: 10.1109/IEMBS.2009.5335278.
In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91+/-5% and 87+/-5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.
在这项工作中，提出了一种用于脑电（EEG）信号特征提取的技术，以对心理任务进行分类，这是脑机接口（BCI）发展的重要组成部分。经验模态分解（EMD）是一种能够处理像EEG这样的非平稳和非线性信号的方法。该技术应用于7名执行5种心理任务的受试者的EEG信号。对于从EMD获得的每个模态和每个EEG通道，计算了六个特征：均方根（RMS）、方差、香农熵、莱姆佩尔-齐夫复杂度值以及中心频率和最大频率，从而获得了一个180维的特征向量。使用威尔克斯' lambda参数来选择最重要的变量，以降低特征向量的维度。使用线性判别分析（LD）和神经网络（NN）对心理任务进行分类。通过这种方法，在数据库中所有受试者上使用LD和NN的平均分类准确率分别为91±5%和87±5%。得出的结论是，与其他传统方法（如频谱分析）相比，EMD在心理任务分类中能获得更好的性能。
