A quality metric to improve wrapper feature selection in multiclass subject invariant brain computer interfaces
Abstract
Brain computer interface systems based on electroencephalograph (EEG) signals
have limitations which challenge their application as a practical device for general use.
The signal features generated by the brain states we wish to detect possess a high degree
of inter-subject and intra-subject variation. Additionally, these features usually exhibit a
low variation across each of the target states. Collection of EEG signals using low
resolution, non-invasive scalp electrodes further degrades the spatial resolution of these
signals. The majority of brain computer interface systems to date require extensive
training prior to use by each individual user. The discovery of subject invariant features
could reduce or even eliminate individual training requirements. To obtain suitable
subject invariant features requires search through a high dimension feature space
consisting of combinations of spatial, spectral and temporal features. Poorly separable
features can prevent the search from converging to a usable solution as a result of
degenerate classifiers. In such instances the system must detect and compensate for degenerate classifier behavior. This dissertation presents a method to accomplish this
search using a wrapper architecture comprised of a sequential forward floating search
algorithm coupled with a support vector machine classifier. This is successfully achieved
by the introduction of a scalar Quality (Q)-factor metric, calculated from the ratio of
sensitivity to specificity of the confusion matrix. This method is successfully applied to a
multiclass subject independent BCI using 10 untrained subjects performing 4 motor tasks.
Table of Contents
Introduction to brain computer interface systems -- Historical perspective and state of the art -- Experimental design -- Degeneracy in support vector machines -- Discussion of research -- Results -- Conclusion -- Appendix A. Information transfer rate -- Appendix B. Additional surface plots for individual tasks and subjects
Degree
Ph.D.