Pre-processing Tool for EEG Signal Visualization

Electroencephalograph (EEG) -based studies that involve human subjects need a continuous monitoring of subjects state for a good data acquisition and research outcome. This paper presents a tool based on frequency domain features for EEG signals visualization. There are some acquisition softwares available but those come with the costly EEG amplifiers. Proposed method focusses on the enhanced presentation of the signal by a visualization tool. Change in frequency-domain features presents the change in state of the subject. Based on the variation in frequency band powers, algorithm displays the change in subject state data with different color codes using time and feature window. Segment length can be changed with user input and according to the prerequisite. Present work uses EEG data set obtained from UCI machine learning repository for evaluating algorithm. The tool is helpful in monitoring the subjects state during data acquisition to maintain the quality of data acquired.