基于神经网络的多参数脑电注意力水平提取

Attention Level Extraction from Multi-Parameters of EEG Based on Neural Networks

为克服利用单一参数判断注意力水平的灵敏性缺陷,本文设计并实现了一种多参数的脑电注意力水平提取方法。首先从脑电信号中提取7个脑电特征参数,全面评估脑电频谱特性,然后利用误差反传(back propagation,BP)神经网络算法实现脑电特征参数与注意力水平间的非线性映射,从而实现准确的注意力水平提取。同时,本文对BP算法进行了一系列的改进,有效地提高了网络的学习效率,并能迅速跳出局部极小。以34例脑电数据对网络进行训练,实验结果证实该网络能迅速收敛,网络收敛后测试库中的脑电数据注意力水平判别结果证明该网络具有较高的识别率。

For the deficit of sensitivity in attention level extraction from a single parameter of EEG, a method to extract attention level from multi-parameters of EEG was designed and implemented. In this method, 7 characteristic parameters, which were used for a full assessment of spectral characteristics of EEG, were first derived, and then the back propagation （BP） neural network algorithm was applied to the nonlinear mapping from 7 parameters to the attention level, so that it could achieve a more accurate attention level extraction. In addition, various improvements were employed on the BP algorithm in order that it could overcome the defect of slow training speed and escape from the local minimum quickly. In the network training using 34 samples, the neural network converged quickly, and the test of attention level extraction after convergence verified that the network had high identification ratio.