An event classifier using EEG signals: An artificial neural network approach

An event classifier has been developed to analyze the collected EEG signals and distinguish between different events. The classifier introduced in this study was based on an artificial neural network model. The training process of the neural network required large amount of sample data and target re...

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Bibliographic Details
Main Authors: Nawroj, A., Siyuan Wang, Jouny, I., Yih-Choung Yu, Gabel, L.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Biological neural networks
Brain modeling
Computers
Electroencephalography
Logistics
Training
Online Access:Request full text
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Summary:An event classifier has been developed to analyze the collected EEG signals and distinguish between different events. The classifier introduced in this study was based on an artificial neural network model. The training process of the neural network required large amount of sample data and target results. A trained artificial neural network can then predict the outcome of an event based on the information of the corresponding EEG signal. The architecture of the artificial neural network involved hidden layers in addition to the input and output layers, which satisfied the non-linearity of the problem that the classifier was designed to solve. Experiments were conducted to validate this approach by using the classifier to distinguish whether subjects placed their fingers into hot or cold water. Validation results demonstrated the effectiveness of the classifier and its potential application in other fields.
ISSN:2160-6986
2160-7028
DOI:10.1109/NEBC.2012.6207126