基于深度压缩感知的脑电情感识别

EEG Emotion Recognition Based on Deep Compressed Sensing

【目的】传统压缩感知中存在观测矩阵对信号适应性和重构算法对字典依赖性的问题,深度压缩感知则利用深度学习的方法解决传统压缩感知中存在的问题。【方法】利用深度信念网络(DBN)能够在不破坏观测矩阵随机性的前提下对信号进行自适应压缩,同时利用栈式自编码器(SAE)可以端到端地训练重构网络来摆脱重构算法对稀疏字典的依赖性,根据信号的稀疏表示中所具有的判别性,提出基于DBN和SAE的压缩感知识别模型(CS-DBN-SAE)。【结果】在DEAP情感脑电数据库上的四分类实验结果表明,CS-DBN-SAE模型的识别率达到83.29%,相比于传统压缩感知识别模型均取得了4.3%以上的提升。

【Purposes】Deep compressed sensing is the use of deep learning to solve the prob-lems existing in traditional compressed sensing,such as the adaptability of observation matrix to traditional signal compression and the dependency on dictionary by reconstruction algorithm.【Methods】In this paper,the deep belief network(DBN)is used to adaptively compress the signal without destroying the randomness of observation matrix.At the same time,the stacked auto encoder(SAE)is used to train the reconstruction network end-to-end to get rid of the dependence of the reconstruction algorithm on sparse dictionary.According to the discrimination of the sparse representation of signal,a compressed sensing recognition model based on DBN and SAE is proposed(CS-DBN-SAE).【Findings】The results of four classification experiments on DEAP emotional EEG database show that the recognition rate of CS-DBN-SAE model is 83.29%,which is oven 4.3%higher than that of traditional compressed sensing recognition model.