Su Kyungmin, Robbins Kay A
Computer Science Department, University of Texas at San Antonio, San Antonio, TX 78249 USA (phone: 210-458-7662; fax: 210-458-4437.
Computer Science Department, University of Texas at San Antonio, San Antonio, TX 78249 USA.
Proc Int Jt Conf Neural Netw. 2013:1-8. doi: 10.1109/IJCNN.2013.6707106.
This paper introduces a prototype framework for content-based EEG retrieval (CBER). Like content-based image retrieval, the proposed framework retrieves EEG segments similar to the query EEG segment in a large database. Such retrieval of EEG can be used to assist data mining of brain signals by allowing researchers to understand the association between brain patterns, responses, and the environment. Retrieval might also be used to enhance the accuracy of Brain Computer Interface (BCI) systems by providing related samples for training. We present key components of CBER and explain how to handle the distinctive characteristics of EEG. To demonstrate the feasibility of the framework, we implemented a simple EEG database of about 37,000 samples from more than 100 subjects. We ran two retrieval scenarios with a set of EEG features and evaluation metrics. The results of finding similar subjects clearly demonstrate the potential of CBER in many EEG applications.
本文介绍了一种基于内容的脑电图检索（CBER）原型框架。与基于内容的图像检索一样，该框架可在大型数据库中检索与查询脑电图段相似的脑电图段。这种脑电图检索可通过让研究人员了解脑模式、反应和环境之间的关联，来辅助脑信号的数据挖掘。检索还可通过提供相关样本来训练，提高脑机接口（BCI）系统的准确性。我们介绍了CBER的关键组件，并说明了如何处理脑电图的独特特征。为了证明该框架的可行性，我们实现了一个包含来自100多名受试者的约37000个样本的简单脑电图数据库。我们使用一组脑电图特征和评估指标运行了两种检索场景。寻找相似受试者的结果清楚地证明了CBER在许多脑电图应用中的潜力。
