In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single tria...In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.展开更多
远离星下点的lead(浮冰间的开阔水域)对回波波形有重要影响,使计算得到的瞬时海面高存在较大偏差。精确地区分出回波波形中的lead波形,能有效提高瞬时海面高(SSH)的测量精度。目前常用Laxon13算法(PP和SSD)来识别回波中的lead波形,进而...远离星下点的lead(浮冰间的开阔水域)对回波波形有重要影响,使计算得到的瞬时海面高存在较大偏差。精确地区分出回波波形中的lead波形,能有效提高瞬时海面高(SSH)的测量精度。目前常用Laxon13算法(PP和SSD)来识别回波中的lead波形,进而计算海冰的出水高度。本文针对Cryosat-2高度计的SARIn模式数据,在Laxon13算法的基础上进行改进,新增了8参数,采用了一种更为精确的lead识别算法,即计算回波能量值在各参数下的统计量,设定分界值(阈值)以识别出lead。该方法采用MAX、PP、PPL、PPR、SSD、LEW、TEW、SLEW、KURT、SKEW共10种参数对lead进行识别,绘制了实验区域基于各个参数的分类图示并识别出lead波形,与Arctic and Antarctic Research Institute(AARI)提供的实际冰况图进行对比分析,确定lead有效识别的5参数。展开更多
基金Natural Science Foundation of Shandong Provincegrant number:Y2007G31
文摘In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.
文摘远离星下点的lead(浮冰间的开阔水域)对回波波形有重要影响,使计算得到的瞬时海面高存在较大偏差。精确地区分出回波波形中的lead波形,能有效提高瞬时海面高(SSH)的测量精度。目前常用Laxon13算法(PP和SSD)来识别回波中的lead波形,进而计算海冰的出水高度。本文针对Cryosat-2高度计的SARIn模式数据,在Laxon13算法的基础上进行改进,新增了8参数,采用了一种更为精确的lead识别算法,即计算回波能量值在各参数下的统计量,设定分界值(阈值)以识别出lead。该方法采用MAX、PP、PPL、PPR、SSD、LEW、TEW、SLEW、KURT、SKEW共10种参数对lead进行识别,绘制了实验区域基于各个参数的分类图示并识别出lead波形,与Arctic and Antarctic Research Institute(AARI)提供的实际冰况图进行对比分析,确定lead有效识别的5参数。
