A provenance driven approach for systematic EEG data analysis

Abstract

As an important issue of Brain Informatics (BI) methodology, systematic brain data analysis has gained significant attractions in BI community. However, the existing expert-driven multi-aspect data analysis and distributed analytical platforms excessively depend on individual capabilities and cannot be widely adopted in systematic human brain study. In this paper, we propose a provenance driven approach for systematic brain data analysis, which is implemented by using the Data-Brain, BI provenances and the Global Learning Scheme for BI. Furthermore, a systematic EEG data analysis for emotion recognition which is a key issue of affective computing is described to demonstrate significance and usefulness of the proposed approach. Such a provenance driven approach reduces the dependency of individual capabilities and provides a practical way for realizing the systematic human brain data analysis of BI methodology.