Analysis and classification of spelling paradigm EEG data and an attempt for optimization of channels used

Yıldırım, Asil
Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. Spelling Paradigm is a BCI application which aims to construct the words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. Reducing the letter detection error rates and increasing the speed of letter detection are crucial for Spelling Paradigm. By this way, disabled people can express their needs more easily using this application. In this thesis, two different methods, Support Vector Machine (SVM) and AdaBoost, are used for classification in the analysis. Classification and Regression Trees is used as the weak classifier of the AdaBoost. Time-frequency domain characteristics of P300 evoked potentials are analyzed in addition to time domain characteristics. Wigner-Ville Distribution is used for transforming time domain signals into time-frequency domain. It is observed that classification results are better in time domain. Furthermore, optimum subset of channels that models P300 signals with minimum error rate is searched. A method that uses both SVM and AdaBoost is proposed to select channels. 12 channels are selected in time domain with this method. Also, effect of dimension reduction is analyzed using Principal Component Analysis (PCA) and AdaBoost methods.


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Spelling Paradigm first introduced by Farwell and Donchin, is one of the Brain Computer Interface (BCI) applications that enables paralyzed people to communicate with their environment. In such a problem, user needs to focus on the characters which are randomly flashed row or column-wise on the computer screen in a small period of time. The accuracy in spelling words is the main problem in this scheme and the duration of the correct prediction is quite important. The purpose of this work is twofold: to anal...
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Akinci, Berna; Gençer, Nevzat Güneri (2009-01-01)
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Brain decoding from brain images obtained using functional magnetic resonance imaging (fMRI) techniques is an important task for the identification of mental states and illnesses as well as for the development of brain machine interfaces. The brain decoding methods that use multi-voxel pattern analysis that rely on the selection of voxels (volumetric pixels) that have relevant activity with respect to the experimental tasks or stimuli of the fMRI experiments are the most commonly used methods. While MVPA ba...
Citation Formats
A. Yıldırım, “Analysis and classification of spelling paradigm EEG data and an attempt for optimization of channels used,” M.S. - Master of Science, Middle East Technical University, 2010.