| 面向事件相关电位成分的稀疏字典构建方法 |
| |
| 引用本文： | 李海峰,丰上,马琳,徐睿峰,徐忠亮.面向事件相关电位成分的稀疏字典构建方法[J].信号处理,2018,34(8):891-896. |
| |
| 作者姓名： | 李海峰 丰上 马琳 徐睿峰 徐忠亮 |
| |
| 作者单位： | 哈尔滨工业大学计算机科学与技术学院 |
| |
| 基金项目： | 国家自然科学基金项目（61671187）;深圳市基础研究项目（JCYJ20150929143955341）;语言语音教育部-微软重点实验室开放基金资助项目(HIT.KLOF.20150XX, HIT.KLOF.20160xx)
|
| |
| 摘 要： | 利用稀疏分解来研究EEG信号是一种新兴的可靠的方法。常见的稀疏字典学习算法中，K-SVD算法得到了比较广泛的应用。但是利用K-SVD算法获得的字典原子很难完整的包含EEG信号中的事件相关电位（ERP）成分,通常样本中的ERP成分都会分布在大量原子中。本文提出把常用的K-SVD算法基础结合稀疏性能指标作为约束条件，解决稀疏字典难以与ERP成分对应的问题，并给出利用该算法进行ERP分析的步骤。通过在一个基于听觉刺激的公开数据集上使用本文的方法，成功地获得了包含完整目标ERP成分的字典原子，证明了方法的可行性。
|
| 关 键 词： | 稀疏字典 事件相关电位 脑电图 成分分析 |
| 收稿时间： | 2018-04-04 |
A Method of Designing Sparse Dictionary for Event-related Potential |
| |
| Affiliation: | School of Computer Science and Technology, Harbin Institute of Technology |
| |
| Abstract: | Sparse decomposition is a trending and reliable paradigm for EEG analysis. Among the common algorithms for sparse decomposition and representation, K-SVD is the most popular one. However, K-SVD cannot extract complete event-related component from EEG into dictionary atoms, and components are usually distributed among many atoms. In this paper we propose a method that combine K-SVD with Sparse Performance Index as constraint, to solve the problem that dictionary atoms are not related to ERP components. Then we propose procedures to analysis EEG data with proposed method. Finally, we demonstrated the capabilities of this method by applying it to a public EEG dataset that contains auditory evoked potential. The result showed that our method can extract complete ERP waveforms into atoms. |
| |
| Keywords: | |
|
| 点击此处可从《信号处理》浏览原始摘要信息 |
|
点击此处可从《信号处理》下载全文 |
|
