A Neural Network Decision Tree for Learning Concepts from EEG Data

Abstract

To learn the multi-class conceptions from the electroencephalogram (EEG) data we developed a neural network decision tree (DT), that performs the linear tests, and a new training algorithm. We found that the known methods fail inducting the classification models when the data are presented by the features some of them are irrelevant, and the classes are heavily overlapped. To train the DT, our algorithm exploits a bottom up search of the features that provide the best classification accuracy of the linear tests. We applied the developed algorithm to induce the DT from the large EEG dataset consisted of 65 patients belonging to 16 age groups. In these recordings each EEG segment was represented by 72 calculated features. The DT correctly classified 80.8% of the training and 80.1% of the testing examples. Correspondingly it correctly classified 89.2% and 87.7% of the EEG recordings.