期刊论文

Emotional Analysis of Brain Waves Based on CNN and Bi-LSTM

Yang, Q.

中文

应用科学学报

0255-8297

2022

40

1

1-12

10.3969/j.issn.0255-8297.2022.01.001

湖北省重点研发计划项目基金（No.2020BAB017）； 武汉市科技计划项目基金（No.2019010701011392）； 国家语委科研中心项目基金（No.ZDI135-135）资助；

本校为第一且通讯机构

要:针对目前大多数脑电波情感识别方法存在的依赖手动特征提取等问题,提出一种基于卷积神经网络（convolutional neural network, CNN）和双向长短时记忆（bidirectional long short-term memory, Bi-LSTM）网络的混合模型。首先将一维数据转换为二维数据,采用CNN提取空间特征;然后将一维数据输入Bi-LSTM,获取时间特征;最后将融合的空间和时间特征输入Softmax分类器,得到最终分类结果。在DEAP数据集上的实验结果表明:CNN和Bi-LSTM混合模型具有较好的分类性能,在效价度和唤醒度上的准确率分别达到88.55%和89.07%,是一种可行的脑电波情感分类模型。

Aiming at the problem that most emotion recognition methods rely on manual feature extraction, a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network is proposed. Firstly, one-dimensional data is converted into two-dimensional data, and spatial features are extracted by CNN. Then the one-dimensional data is input into Bi-LSTM to obtain temporal features. Finally, the fused spatial and temporal features are input into Softmax classifier to obtain final classification results. Experim...