一种随机隐退DBN的脑电信号识别方法

针对DBN处理小样本脑电信号训练时间长且存在过拟合的问题,提出基于随机隐退的DBN算法对左右手运动想象脑电信号进行分类识别.先对原始脑电数据进行降维预处理,然后输入到随机隐退DBN模型中进行训练,得到最优参数值后进行分类识别.实验结果表明:与CSP、PCA、单一DBN网络等方法相比,基于随机隐退的DBN算法在保持较高识别率的同时,降低了对数据集的训练处理时间,证明了该方法的有效性.最后在智能轮椅平台上验证了该算法的可行性.

An improved random retreat DBN recognition method for EEG signals

To solve the problem of long training time and over-fitting of small sample EEG signal processing, this paper proposes a DBN based on random retreat algorithm, which can classify and identify the left and right hand motion imaginary EEG signals. Firstly, the original EEG data were processed by dimension reduction, and the random DBN model was used to train the reduced EEG data, then the optimal parameter values for classification and recognition were obtained. The experimental results show that compared with CSP, PCA and single DBN network, the DBN algorithm based on random retreat can maintain the high recognition rate and reduces the training time, which proves the effectiveness of the method. Finally, the feasibility of the algorithm was verified on the intelligent wheelchair platform.