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14 November 2007 Application of ensemble classifier in EEG-based motor imagery tasks
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Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 678913 (2007) https://doi.org/10.1117/12.750287
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Electroencephalogram (EEG) recorded during motor imagery tasks can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel for the subjects with neuromuscular disorders. To achieve higher speed and more accuracy to enhance the practical applications of BCI in computer aid medical systems, the ensemble classifier is used for the single classification. The ERDs at the electrodes C3 and C4 are calculated and then stacked together into the feature vector for the ensemble classifier. The ensemble classifier is based on Linear Discriminant Analysis (LDA) and Nearest Neighbor (NN). Furthermore, it considers the feedback. This method is successfully used in the 2003 international data analysis competition on BCI-tasks (data set III). The results show that the ensemble classifier succeed with a recognition as 90%, on average, which is 5% and 3% higher than that of using the LDA and NN separately. Moreover, the ensemble classifier outperforms LDA and NN in the whole time course. With adequate recognition, ease of use and clearly understood, the ensemble classifier can meet the need of time-requires for single classification.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bianhong Liu and Hongwei Hao "Application of ensemble classifier in EEG-based motor imagery tasks", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 678913 (14 November 2007); https://doi.org/10.1117/12.750287
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