EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Abstract

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

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

“…The accuracy towards the classification changed very less and this analysis outcome shown in table [3], figure [6]. The Confusion matrix shown in figure [5] and ROC curve shown in figure [7], evaluate the classifier performance here the classifier is Instance based classifier (K*), the classification accuracy is computed and it is mapped in table [3]. CONCLUSION This is the first study to investigate the characteristics of Most Non Dominant feature from feature space they are less responsible to build the classification model, the MND set always gives concept which feature removal sufficiently reduce space and time requirement to build the classification model.…”

“…The instance based classifier is a type of lazy classifier [27], and proposed method uses K* is a type of instance base classifier, after extreme value removal and attribute selection. The literature shows there are various statistical measures are used for analysis of classification outcomes generated from classification process [29][30][31][32].…”

“…These are the motivational points for proposed work because the article finds those positions electrode they are less responsible for classification then the removal of those electrodes minimize the size of devices. The present work is performed with EEG electrode data having 16 electrodes and 14892 instances [4,5]. This uses the instance based classifier (K*), because based on statistic of data and nature of data spread over the corpus found it is best among other classifier the result of this present in literature [6,7], [28], [33], [38].…”

EEG signal classification using PSO trained RBF neural network for epilepsy identification

“…Set C, D and E have been collected from epileptic patients, but C and D recorded in seizure-free activity, where set E contains seizure activity. EEG data for eye state prediction is already in a sample -feature format for classification problem [41]. …”

Performance of Classification Techniques on Medical Datasets

“…IAL is a novel machine learning strategy which gradually imports and trains features one by one. IAL exhibited better classification performance in terms of classification error rates in comparison with conventional and some other approaches [14]. Roesler and Suendermann (2013) applied 42 different machine learning algorithms to the related dataset to predict the eye state after training with the corpus.…”