Novel Cluster-Based SVM to reduce classification error in noisy EEG data: towards real-time brain-robot interfaces

Abstract

To be able to control a robotic platform using signals form the human brain is something that has been considered science fiction for a long time. With the technology available today however, it has more or less become a reality. Electroencephalography or EEG for short is a widely used method for extracting signals form the brain. The signals in this thesis contains motor imagery (MI) commands that are supposed to be sent through a brain computer interface (BCI) to control a mobile robot. This thesis investigates deeper in-to classification of these signals, specifically optimising classification accuracy of noisy EEG data, that previously has been unsatisfactory classified with support vector machine (SVM). It is paramount that the classification accuracy is as high as possible when used in a BCI since the robot can cause damage if the commands are faulty. A new cluster-based SVM is developed that discards uncertain trials and minimises the false positive rate in an attempt to increase the accuracy. The algorithm increases the classification accuracy compared to SVM alone by 8 percentage points. Alongside this new algorithm, eye movement artefacts and separability of the MI commands are analysed to further investigate classification accuracy influences.