基于小波分解和支持向量机的P300识别算法

针对支持向量机方法在P300识别中训练和识别速度相对较慢的不足,本研究提出了一种将小波分解与支持向量机相结合的P300识别方法。该方法通过小波分解实现脑电信号的特征提取,同时利用Span估计方法实现支持向量机最优参数的快速选择;然后借助支持向量机良好的分类性能实现P300的识别。本研究在BCICompetition 2003的P300实验数据集上对该方法进行了验证,结果表明,与传统支持向量机算法相比,本算法具有更高的训练和识别速度,并且在5次重复实验时达到了100%的识别准确率。

P300 Detection Algorithm Based on Wavelet Decomposition and Support Vector Machines

In order to overcome the shortcomings of support vector machines in terms of low training and detection speed for P300 detection,a new algorithm based on wavelet decomposition and support vector machines was proposed in this paper.Using wavelet decomposition and span estimation method,we implemented the feature extraction of EEG signals and the fast choice of the optimal parameters of support vector machines;and realized the detection of P300 component with support vector machines,which have a good classification performance.The algorithm was tested with a P300 dataset from the BCI competition 2003.The results showed that the algorithm designed in this paper was superior to the traditional support vector machines algorithms in terms of the training and detection speed.Using this algorithm,we achieved an accuracy of 100% in P300 detection within five repetitions.