欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
摘要提出一种利用小波包变换和支持向量机对手部动作的运动想象脑电信号进行分类的方法．在相关眼动辅助情况下采集想象手部动作时的C3、C4、P3和P4通道脑电信号，用小波包变换的方法提取4种特征节律波，分别计算每种节律波能量占4种节律波能量之和的比值作为特征，然后将16维特征向量输入支持向量机分类器进行手部动作分类．对上翻、下翻、展拳、握拳4种手部动作的分类实验中平均识别率为82．3％，表明眼动辅助能有效提高运动想象脑电信号可分性．
英文摘要：
A classification method is presented to classify the using wavelet packet transform （WPT） and support vector machine （SVM） motor imagery electroencephalogram （EEG）of hand motion. Firstly, the relevant eye-moving assisted EEG at C 3, C 4, P 3 and P 4 during hand-motion imagery are recorded. Then, four feature rhythm waves are extracted using WPT, and the ratio of energy of each rhythm wave to the sum energy of all four rhythm waves is calculated respectively as the feature. Finally, the 16 dimension feature vector is input into SVM classifier to recognize the hand-motions. The average correct rate of four patterns of hand motions, namely wrist extension, wrist flexion, hand opening and hand grasping, is 82. 3% in classification experiments and it shows that eye-moving assist improves the separability of motor imagery EEG.
同期刊论文项目
同项目期刊论文
期刊信息
位置：