title: Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI.
abstract: Many of the most widely accepted methods for reliable detection of steady-state visual evoked potentials (SSVEPs) in the electroencephalogram (EEG) utilize canonical correlation analysis (CCA). CCA uses pure sine and cosine reference templates with frequencies corresponding to the visual stimulation frequencies. These generic reference templates may not optimally reflect the natural SSVEP features obscured by the background EEG. This paper introduces a new approach that utilizes spatio-temporal feature extraction with multivariate linear regression (MLR) to learn discriminative SSVEP features for improving the detection accuracy. MLR is implemented on dimensionality-reduced EEG training data and a constructed label matrix to find optimally discriminative subspaces. Experimental results show that the proposed MLR method significantly outperforms CCA as well as several other competing methods for SSVEP detection, especially for time windows shorter than 1 second. This demonstrates that the MLR method is a promising new approach for achieving improved real-time performance of SSVEP-BCIs.
authors.forename: Haiqiang
authors.initials: H
authors.lastname: Wang
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Nicholas R
authors.initials: NR
authors.lastname: Waytowich
authors.forename: Dean J
authors.initials: DJ
authors.lastname: Krusienski
authors.forename: Guoxu
authors.initials: G
authors.lastname: Zhou
authors.forename: Jing
authors.initials: J
authors.lastname: Jin
authors.forename: Xingyu
authors.initials: X
authors.lastname: Wang
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000328:Adult; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult