Perseh Bahram, Sharafat Ahmad R
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
J Med Signals Sens. 2012 Jul;2(3):128-43.
We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain-computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann's dataset for eight subjects, and achieved similar results.
我们提出了一种新颖且高效的方案，该方案为在脑机接口（BCI）范式中检测事件相关电位的P300成分选择一组最小的有效特征和通道。为了获得一组最小的有效特征，我们采用离散Daubechies 4小波的截断系数，并且为了选择有效的脑电图通道，我们将改进的二进制粒子群优化算法与Bhattacharyya准则一起使用。我们在2005年BCI竞赛的数据集IIb上测试了我们提出的方案，使用基于贝叶斯线性判别分析的简单分类算法，在15次和5次试验中分别达到了97.5%和74.5%的准确率。我们还在霍夫曼数据集上对八名受试者测试了我们提出的方案，并取得了类似的结果。
