中文摘要：

单次脑电分类实验中，采用基于logistic回归的正则化方法来提高分类准确率．首先，提出一种新算法——局部保持投影稀疏logistic回归，将局部保持投影正则项加入到稀疏logistic回归中．该算法旨在保留原始特征空间邻域信息的同时保证结果的稀疏性．然后，利用边界优化法和逐分量迭代算法在训练集上求解权重向量，克服了牛顿一拉夫森法和迭代重加权最小二乘法的局限性．最后，在自步调手指运动数据集上采用十重交叉验证法得到80％的分类准确率，并与稀疏logistic回归的实验结果进行对比，说明局部保持投影正则项有效地保留了对脑电分类有用的信息．

英文摘要：

In order to improve classification accuracy, the regularized logistic regression is used to classify single-trial electroencephalogram （EEG）. A novel approach, named local sparse logistic regression （LSLR）, is proposed. The LSLR integrates the locality preserving projection regularization term into the framework of sparse logistic regression. It tries to maintain the neighborhood information of original feature space, and, meanwhile, keeps sparsity. The bound optimization algorithm and component-wise update are used to compute the weight vector in the training data, thus overcoming the disadvantage of the Newton-Raphson method and iterative re-weighted least squares （IRLS）. The classification accuracy of 80% is achieved using ten-fold cross-validation in the self-paced finger tapping data set. The results of LSLR are compared with SLR, showing the effectiveness of the proposed method.