Some limitations of localizing inert region from eeg

Investigation of the diagnosis of human brain through the electroencephalograph (EEG) is an important application of EEG signals. While automated techniques exist for EEG analysis, it is likely that additional information can be extracted from EEG signals through the use of new methods. In this paper, we propose a method that applies artificial neural networks approach for identification of the inactive brain region from EEG signals. Let us assume for a while that inert region. The method has three main steps. First, a large scale, complex EEG signals is simply normalized into a reasonable data. Second, characteristic feature from EEG signals, the normalized EEG data has no regular rule and is still so complex that neural network can not learn the characteristic feature from EEG. Hence we extract characteristic feature from EEG and use root mean square (RMS) processes in EEG data. Finally, neural network is supplied as the input value to these processes in EEG and learn these characteristic feature. To demonstrate the effectiveness of the method, we perform simulations on location of inert region from EEG data, consists of training and test data. These EEG estimation tasks were created by using a set of calculated, artificial EEG signals based on a number of current dipoles. The experimental results indicate that the proposed method has several attractive features. 1) The method can estimate the EEG feature of inert region and better generalization performance can be achieved than non preprocessing EEG. 2) The more larger inert region were, the more small estimation error become. 3) If the sampling point of EEG signals is large, the estimation error will grow smaller.