Time Series Classification for EEG Eye State Identification Based on Incremental Attribute Learning

Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.