中文摘要：

针对想象运动的脑机接口（BcI）系统存在分类准确率低、抗干扰能力差等不足，提出一种将离散小波变换（DWT）和BP神经网络相结合的脑电识别方法（DWT—BP法）。通过计算想象左、右手运动的C3、C4的平均功率，合理确定时间窗设置，对时间窗内的平均功率信号进行离散小波变换，并选取尺度6上的逼近系数A6的组合信号作为脑电信号特征，以BP神经网络为分类器实现对脑电观测数据的分析。实验结果表明，DWT-BP方法能够较准确地提取脑电信号的本质特征，具有较好的抗干扰能力和分类性能，以及识别运动想象脑电信号的有效性，同时为实现运动想象在线BCI系统打下基础。

英文摘要：

With the aim to solve the problems such as low classification accuracy and weak anti-disturbances in braincomputer interfaces （BCIs） of imaging movement, a new method for the recognition of electroencephalography （EEG） was proposed in this work, which combined the discrete wavelet transform （DWT） and BP neural network （DWT-BP）. A rational time window was set by calculating the average power of C3 electrode and CA electrode in imaging left or right hand movement. Then the average power within the time window was taken into DWT. The combinational signal of approximation coefficient A6 on the sixth level was selected as EEG feature, and BP neural network was used as classifier. The obtained EEG data was analyzed by the BP network. The experimental results showed that the proposed method could accurately extract substantial features of EEG and display better anti-disturbances and classification performance. The method is effective for EEG recognition of imaging movements, which provides a basis for realizing on-line BCI systems of imaging movements.