title: BCI Competition IV - Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection.
abstract: Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research Singapore.
authors.forename: Haihong
authors.initials: H
authors.lastname: Zhang
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Chuanchu
authors.initials: C
authors.lastname: Wang