The traditional way of electroencephalographic data analysis is visual inspection. Expert's classification may not always correspond completely with measured data due to the subjective evaluation and the fact that this kind of evaluation is tedious and time consuming. This paper presents a decision support tool developed both for clinical and nonclinical applications in this field. The proposed solution comprises several consecutive steps of signal preprocessing and processing, with focus on segmentation, feature extraction, classification and consequent visualization. This way additional information can be provided and presented in a convenient form.
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