Challenges With Session to Session Managementin Brain Computer Interfaces : A Comparison of Classification Methods for Motor Imagery Induced EEG Patterns

Sammanfattning: Brain computer interfaces (BCIs) enable communication between a brain and a computer, without the need for any motor actions. Electroencephalography (EEG) signals can be used as input for a BCI, but they need to go through a number of steps in the BCI to create useableoutput. One of the most critical steps is the classification algorithm,which is the step that is investigated in this report. A linear and anonlinear Support Vector Machine (SVM), together with a Linear Discriminant Analysis (LDA), are investigated in how well they can handlesession to session performance when classifying EEG data from three different recording sessions of three different test subjects. The results show that the average performance of the classifiers are in most cases similar, slightly above 60 %. The performance of the investigated algorithms differed depending on subject and session. The sometime slow performance of the classification algorithms may be due to the lowsignal-to-noise ratio in the EEG signals, or possibly even due to bad performance in producing recognizable EEG patterns by the test subjects.The conclusion drawn from the project is that data from different sessions can vary quite extensively, and in this project it was handledbest by the nonlinear SVM with RBF kernel, with the highest averageclassification accuracy.