Detecting error related negativity using EEG potentials generated during simulated brain computer interaction
Date
2014
Authors
Verlekar, Prathamesh, author
Anderson, Charles, advisor
Ruiz, Jaime, committee member
Davies, Patricia, committee member
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Abstract
Error related negativity (ERN) is one of the components of the Event-Related Potential (ERP) observed during stimulus based tasks. In order to improve the performance of a brain computing interface (BCI) system, it is important to capture the ERN, classify the trials as correct or incorrect and feed this information back to the system. The objective of this study was to investigate techniques to detect presence of ERN in trials. In this thesis, features based on averaged ERP recordings were used to classify incorrect from correct actions. One feature selection technique coupled with four classification methods were used and compared in this work. Data were obtained from healthy subjects who performed an interaction experiment and the presence of ERN indicating incorrect responses was studied. Using suitable classifiers trained on data recorded earlier, the average recognition rate of correct and erroneous trials was reported and analyzed. The significance of selecting a subset of features to reduce the data dimensionality and to improve the classification performance was explored and discussed. We obtained success rates as high as 72% using a highly compact feature subset.
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Subject
RFE
EEG
SVM
brain computer interface
machine learning
neural network