Knowledge Commons of Institute of Automation,CAS
EEG-Based Motor Imagery Classification with Deep Multi-Task Learning | |
Yaguang Song1; Danli Wang1; Kang Yue1![]() ![]() | |
2019 | |
会议名称 | In 2019 International Joint Conference on Neural Networks (IJCNN) |
页码 | 1-8 |
会议日期 | July 14-19, 2019 |
会议地点 | Budapest, Hungary |
摘要 | In the past decade, Electroencephalogram (EEG) has been applied in many fields, such as Motor Imagery (MI) and Emotion Recognition. Traditionally, for classification tasks based on EEG, researchers would extract features from raw signals manually which is often time consuming and requires adequate domain knowledge. Besides that, features manually extracted and selected may not generalize well due to the limitation of human. Convolutional Neural Networks (CNNs) plays an important role in the wave of deep learning and achieve amazing results in many areas. One of the most attractive features of deep learning for EEG-based tasks is the end-to-end learning. Features are learned from raw signals automatically and the feature extractor and classifier are optimized simultaneously. There are some researchers applying deep learning methods to EEG analysis and achieving promising performances. However, supervised deep learning methods often require large-scale annotated dataset, which is almost impossible to acquire in EEG-based tasks. This problem limits the further improvements of deep learning models for classification based on EEG. In this paper, we propose a novel deep learning method DMTL-BCI based on Multi-Task Learning framework for EEG-based classification tasks. The proposed model consists of three modules, the representation module, the reconstruction module and the classification module. Our model is proposed to improve the classification performance with limited EEG data. Experimental results on benchmark dataset, BCI Competition IV dataset 2a, show that our proposed method outperforms the state-of-the-art method by 3.0%, which demonstrates the effectiveness of our model. |
收录类别 | EI |
七大方向——子方向分类 | 知识表示与推理 |
国重实验室规划方向分类 | 社会系统建模与计算 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57074 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of California, Berkeley |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yaguang Song,Danli Wang,Kang Yue,et al. EEG-Based Motor Imagery Classification with Deep Multi-Task Learning[C],2019:1-8. |
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 | ||
EEG-Based_Motor_Imag(1029KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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