Palaniappan Ramaswamy
Department of Computer Science, University of Essex, Colchester, CO4 3SQ, UK.
IEEE Trans Neural Syst Rehabil Eng. 2006 Sep;14(3):299-303. doi: 10.1109/TNSRE.2006.881539.
A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.
设计脑机接口（BCI）的一种常用方法是使用在心理任务期间提取的脑电图（EEG）信号。在这些BCI设计中，来自EEG的特征，如δ、θ、α和β波段的功率和不对称率，已被用于对不同的心理任务进行分类。在本文中，除了低频波段外，还通过使用γ（24 - 37 Hz）波段的频谱功率和不对称率来提高基于心理任务的BCI设计的性能。在实验研究中，使用了从四名受试者在五项心理任务期间提取的EEG信号。通过弹性反向传播算法训练的埃尔曼神经网络（ENN）被用于将EEG的功率和不对称率分类为两种心理任务的不同组合。结果表明：（1）通过使用额外的γ波段特征，BCI设计的分类性能和训练时间得到了提高；（2）分类性能几乎不受ENN隐藏单元数量或特征提取方法的影响。
