Journal of Neurology and Neurorehabilitation Research

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Channel selection using visual data mining and pattern matching techniques on electroencephalograph (EEG) sensor data.

The amount of data collected from different biomedical devices is increasing rapidly due to various disease and disorder detection. The use of visual data mining techniques on biomedical device is an interactive tool for bio-medical data analysis. This paper introduces concepts and algorithm for channel selectionthrough channel ranking using visual data mining technique. The aim is to propose a novel algorithm for channel ranking and ranked channels performance is evaluated using classification. Channel ranking toward classification is a task to find relevant channels that are minimal subset of channels, which are essential to group the data in a meaningful way. To extract useful information for classification on biomedical devices; visual data mining technique combines traditional data mining with information visualization. Information visualization on biomedical device uses statistical and artificial intelligence technique for data exploration and analysis. There are various types of biomedical devices like EEG, ECG, EMG, EOG etc. Multichannel EEG device is used to show the useful of proposed work. EEG device is normally used in brain computer interface and many other applications. The proposed algorithm gives optimal channel ranking to rank the channel as per their importance in increasing order of their relevancy for EEG eye state recognition. To rank the channels proposed methodology combines Quad Tree data structure and SIFT (Image Matching) algorithm. Performance of proposed method is evaluated and compared with different channel ranking algorithms. The proposed algorithm is remarkable and comparable with conventional algorithms for channel ranking. The maximum accuracy on EEG eye state classification founds using proposed algorithm is 89.05 %.

Author(s): Mridu Sahu