Siuly, and Li, Yan and Wu, Jinglong and Yang, Jingjing (2011) Developing a logistic regression model with cross-correlation for motor imagery signal recognition. In: 2011 IEEE/ICME International Conference on Complex Medical Engineering (CME 2011), 22-25 May 2011, Harbin, China.
Classification of motor imagery (MI)-based electroencephalogram (EEG) signals is a key issue for the development of brain-computer interface (BCI) systems. The objective of this study is to develop an algorithm that can distinguish two categories of MI EEG signals. In this paper, we propose a new classification algorithm for two-class MI signals recognition in BCIs. The proposed scheme develops a novel crosscorrelation-based feature extractor, which is aided with a logistic regression model. The present method is tested on dataset IVa of BCI Competition III, which contain two-class MI data for five subjects. The performance is objectively computed using a k-fold cross validation (k=10) method on the testing set for each subject. The results of this study are compared with the recently reported eight methods in the literature. The results demonstrate that our proposed method outperforms the eight methods in terms of the average classification accuracy.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Uncontrolled Keywords:||electroencephalogram (EEG); brain-computer interface (BCI); motor imagery (MI); cross-correlation technique; logistic regression model|
|Depositing User:||Mrs . Siuly|
|Date Deposited:||05 Jan 2012 05:09|
|Last Modified:||24 Feb 2013 23:48|
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