Mr. Bianhong Liu Profile

Bianhong Liu

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Proceedings Article | 14 November 2007 Paper

Application of ensemble classifier in EEG-based motor imagery tasks

Bianhong Liu, Hongwei Hao

Proceedings Volume 6789, 678913 (2007) https://doi.org/10.1117/12.750287

KEYWORDS: Brain-machine interfaces, Computing systems, Electroencephalography, Telecommunications, Human-machine interfaces, Electrodes, Data analysis, Data modeling, Classification systems, Brain

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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.

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