A Framework for Content-based Retrieval of EEG with Applications to Neuroscience and Beyond.

Abstract

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.