استخراج ويژگي ها با استفاده از اطلاعات متقابل جهت طبقه بندي سيگنال هاي مغزي در سيستم هاي ارتباطي مغز با كاميپوتر

Feature Extraction Using Mutual Information for Classification of Electroencephalogram in Brain Computer Interface

Reducing the number of features is essential to improve the accuracy, efficiency and scalability of a classification process. There are two main reasons to keep the dimensionality of the input features: computational cost and classification accuracy. Reducing the number of input features can be done by selecting relevant features (i.e., feature selection) or extracting new features containing maximal information about the class label from the original ones (i.e., feature extraction). In this work, we use a mutual information based feature extraction (MIFX) algorithm for classification of electroencephalogram (EEG) in brain-computer interface (BCI). The tasks to be discriminated are the imaginative hand movement and the resting state. The experiments were conducted on four healthy subjects on different days. The results show that the classification accuracy obtained by MIFX is higher than that achieved by full feature set. Moreover, the results indicate that the performance obtained using MIFX is higher than that obtained using principle component analysis.