Finding Non Dominant Electrodes Placed in Electroencephalography (EEG) for Eye State Classification using Rule Mining

Electroencephalography is a measure of brain activity by wave analysis; it consist number of electrodes. Finding most non-dominant electrode positions in Eye state classification is important task for classification. The proposed work is identifying which electrodes are less responsible for classification. This is a feature selection step required for optimal EEG channel selection. Feature selection is a mechanism for subset selection of input features, in this work input features are EEG Electrodes. Most Non Dominant (MND), gives irrelevant input electrodes in eye state classification and thus it, reduces computation cost. MND set creation completed using different stages. Stages includes, first extreme value removal from electroencephalogram (EEG) corpus for data cleaning purpose. Then next step is attribute selection, this is a preprocessing step because it is completed before classification step. MND set gives electrodes they are less responsible for classification and if any EEG electrode corpus wants to remove feature present in this set, then time and space required to build the classification model is (20%) less than as compare to all electrodes for the same, and accuracy of classification not very much affected. The proposed article uses different attribute evaluation algorithm with Ranker Search Method. Keywords—Electroencephalography (EEG); Most Non Dominant (MND); Ranker algorithm; classification; EEG