The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2015, Vol 3, Issue 4
Abstract
What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm. In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.
Authors and Affiliations
Kadir Sabancı| Karamanoglu Mehmetbey University, Faculty of Engineering, ElectricalElectronic Engineering Department, Karaman, Turkey, Murat Koklu*| Selcuk University, Faculty of Technology, Computer Engineering, Department, Konya, Turkey
Keywords
Related Articles
- EP ID EP785
- DOI 10.18201/ijisae.75836
- Views 422
- Downloads 44
Classification of Wheat Types by Artificial Neural Network
In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach. Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our syst...
The Principal Component Analysis Method Based Descriptor for Visual Object Classification
In the field of machine learning, which values / data labeling or recognition is done by pattern recognition. Visual object classification is an example of pattern recognition, which attempts prompt to assign each object...
Estimating of Compressive Strength of Concrete with Artificial Neural Network According to Concrete Mixture Ratio and Age
Compressive strength of concrete is one of the most important elements for an existing building and a new structure to be built. While obtaining the desired compressive strength of concrete with an appropriate mix and cu...
Using Word Embeddings for Ontology Enrichment
Word embeddings, distributed word representations in a reduced linear space, show a lot of promise for accomplishing Natural Language Processing (NLP) tasks in an unsupervised manner. In this study, we investigate if the...
Predicting Student Success in Courses via Collaborative Filtering
Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that...