利用频带能量和k近邻分类器进行皮层脑电图分类

脑-机接口系统是一个不依靠外周神经和肌肉而实现大脑和外部设备之间进行直接的交流和控制的通道。对一个典型的采用皮层脑电图的植入式脑-机接口系统进行了离线分析。首先,采用频带能量特征进行导联的选择,从64导联中获取特征最明显的11导进行分析;然后,采用采用频带能量对11导皮层脑电图进行特征提取,得到22维的特征矢量;最后,采用采用k近邻分类器对两类意识任务(想象左手小手指运动或舌头运动)进行分类。离线分析结果表明,该方法对测试数据取得了很好的分类准确率。

ECoG signals classification using band power and k nearest neighbor

Brain-Computer Interface (BCI) systems support direct communication and control between brain and external devices without use of peripheral nerves and muscles. A typical Electrocorticography (ECoG) based invasive BCI system was off-line analyzed in this paper. Firstly, Band Power (BP) features were used for channels selection, and 11 channels with distinctive features were selected from 64 channels. Then, BP features were used for feature extraction of 11 channels ECoG, and feature vectors of 22 dimensions were got. Finally, k Nearest Neighbor (kNN) was used for classification of two different mental tasks (imaged movement of left finger or tongue). The off-line analysis results show that this method has got good classification accuracy for the test data set.