title: A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.
abstract: Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei, China.|School of Computer Science and Technology, Anhui University, Hefei, China.
authors.forename: Bangyan
authors.initials: B
authors.lastname: Zhou
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei, China.|School of Computer Science and Technology, Anhui University, Hefei, China.
authors.forename: Xiaopei
authors.initials: X
authors.lastname: Wu
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei, China.|School of Computer Science and Technology, Anhui University, Hefei, China.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Lv
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei, China.|School of Computer Science and Technology, Anhui University, Hefei, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei, China.|School of Computer Science and Technology, Anhui University, Hefei, China.
authors.forename: Xiaojin
authors.initials: X
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D001331:Automation; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D019018:Imagery, Psychotherapy