A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface

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...

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Bibliographic Details
Published in:PloS one 2016-09, Vol.11 (9), p.e0162657-e0162657
Main Authors: Zhou, Bangyan, Wu, Xiaopei, Lv, Zhao, Zhang, Lei, Guo, Xiaojin
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Automation
Biology and Life Sciences
Biomedical engineering
Brain
Brain-Computer Interfaces
Classification
Computer applications
Computer science
Data collection
Education
EEG
Electroencephalography
Engineering
Engineering and Technology
Experiments
Human-computer interface
Humans
Image classification
Imagery, Psychotherapy
Implants
Independent component analysis
Laboratories
Medicine and Health Sciences
Mental task performance
Methods
Multichannel communication
Optimization
Physical Sciences
Research and Analysis Methods
Signal processing
Spatial filtering
Training
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0162657