Implementation of EEG Emotion Recognition System Based on Hierarchical Convolutional Neural Networks | |
Jinpeng Li; Zhaoxiang Zhang; Huiguang He | |
2016-11-28 | |
会议名称 | The eighth International Conference on Brain Inspired Cognitive Systems |
会议录名称 | BICS 2016 |
会议日期 | 28-30 November 2016 |
会议地点 | Beijing, China |
摘要 | Deep Learning (DL) is capable of excavating features hidden deep in complex data. In this paper, we introduce hierarchical convolutional neural networks (HCNN) to implement the EEG-based emotion classifier (positive, negative and neutral) in a movie-watching task. Differential Entropy (DE) is calculated as features at certain time interval for each channel. We organize features from different channels into two dimensional maps to train HCNN classifier. This approach extracts features contained in the spatial topology of electrodes directly, which is often neglected by the widely-used one-dimensional models. The performance of HCNN was compared with one-dimensional deep model SAE (Stacked Autoencoder), as well as traditional shallow models SVM and KNN. We find that HCNN (88.2% ± 3.5%) is better than SAE (85.4% ± 8.1%), and deep models are more favorable in emotion recognition BCI (Brain-computer Interface) system than shallow models. Moreover, we show that models learned on one person is hard to transfer to others and the individual difference in EEG emotion-related signal is significant among peoples. Finally, we find Beta and Gamma (rather than Delta, Theta and Alpha) waves play the key role in emotion recognition. |
关键词 | Emotion Recognition Eeg Deep Learning Hcnn Brain Wave |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/13305 |
专题 | 模式识别实验室 |
通讯作者 | Huiguang He |
作者单位 | Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing, Peoples R China |
第一作者单位 | 类脑智能研究中心 |
推荐引用方式 GB/T 7714 | Jinpeng Li,Zhaoxiang Zhang,Huiguang He. Implementation of EEG Emotion Recognition System Based on Hierarchical Convolutional Neural Networks[C],2016. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
bics.pdf(2786KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论