Ince Nuri F, Tewfik Ahmed H, Arica Sami
Department of Electrical and Computer Engineering, University of Minnesota, MN 55455, USA.
Comput Biol Med. 2007 Apr;37(4):499-508. doi: 10.1016/j.compbiomed.2006.08.014. Epub 2006 Sep 29.
We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.
我们提出了一种新的自适应时频平面特征提取策略，用于对脑机接口任务中与左右手握力想象相对应的脑电图（EEG）进行分割和分类。该算法通过在二进树上将EEG数据划分为非均匀时间段，自适应地分割时间轴。然后，对每个时间段内频率轴上的扩展系数进行分组。从分割后的时频平面中选择最具判别力的特征，并将其输入到线性判别器中进行分类。通过为每个受试者选择最具判别力的子空间，该算法在六个受试者上实现了84.3%的平均分类准确率。作为比较，还给出了基于自回归模型的分类结果，同一受试者的平均准确率为79.5%。有趣的是，每个受试者及其两个半球由不同的分割和特征表示。这表明所提出的方法在构建脑机接口时能够处理受试者间的变异性。
