演化级联神经网络对脑电信号分类的研究

Research on EEG Classification with Evolving Cascade Neural Networks

为了对不同思维状态下的脑电信号进行正确分类,克服神经网络分类器受噪声和冗余特征的影响出现的过拟合,提出了一种新的演化级联神经网络的学习算法。算法通过计算神经元对确认集的适应函数值,以逐步更新神经元对训练集的连接权重。适应函数值取决于被训练神经元的泛化能力,它随着神经元分类准确度的增加而降低。此网络由一个输入节点开始学习,随着演化增加新的输入神经元及新的隐神经元,最终经训练的网络含有最小数目的神经元及连接。此方法应用于区分两种思维状态下的脑电信号,经训练的网络对测试段的分类正确率为83.1%,与标准的BP网络进行比较,演化级联神经网络显示了较好的分类能力。

To correctly classify EEG with different mental tasks, a new learning algorithm for Evolving Cascade Neural Networks （ECNNs） is described to avoid over-fitting of a neural network due to noise and redundant features. The learning algorithm calculates the value of a fitness function on validate set and accordingly updates the connection weights on training set. The learning algorithm uses the regularity criterion for selecting the neurons with relevant connection. If the value Cr calculated for the rth neuron is less than the value Cr-1 calculated for the previous （r-1） neuron, the features that feed the rth neuron are relevant, else they are irrelevant. An ECNN starts to learn with one input node and then, adding new inputs as well as new hidden neurons, evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm is applied to classify EEG with two mental tasks. The trained ECNN has correctly classified 83.1% of the testing segments. It shows a better result, compared with a standard BP network.