Analysis of Electroencephalography (EEG) Signals using Visualization Techniques

The feasibility of machine learning and data mining has been recognized by past research in the analysis of biomedical device data. The proposed work analyses the biomedical Electroencephalography (EEG) device data using visualization techniques and these techniques have potential for extracting information from huge data sets. The presented work includes extraction of visual patterns using different visualization techniques. The pattern identification task is performed in several stages like outlier removal from EEG. After outlier detection data is divided into manageable segments to avoid overcrowding of data points. Then random method is applied to select a segment and identify prominent patterns by application of visual mapping techniques.