Analysis of Anticipation Related Potentials for Brain Computer Interaction

Anticipation is a mental process during which a person actively engages in a phase required for the sensory perception and execution of the optimal actions at the arrival of the relevant future events. Since this process occurs before the execution of an intended action, it may be used as a control signal for Brain Computer Interface (BCI) applications. Recognition of neural correlates of this process can enhance the performance of a BCI and in turn reduce mental workload of its users. To this end, it is vital to understand the neural correlates involved in this process and to design robust methods for its recognition in single trials. The analysis of these correlates may also contribute to the basic knowledge of the mechanisms underlying this behavior. The thesis provides three major contributions: (i) it reports methods for the robust recognition of anticipation related Electroencephalogram (EEG) potentials (ii) it provides insights into the selection of appropriate preprocessing steps required for enhancing the Signal-to-Noise Ratio (SNR) of anticipatory slow cortical potentials (SCPs) and (iii) it identifies scalp area specific oscillatory activity related to different aspects of anticipatory behavior. First, we focus on methods for the single trial recognition of anticipatory SCPs using the widely known classical contingent negative variation (CNV) paradigm. Using this paradigm, we demonstrate the feasibility of recognizing the anticipatory SCPs (CNV potentials) using features thatmodel its temporal pattern. We propose a Bayesian approach that exploits temporal evolution and redundancy to quickly classify (e.g., within half of the anticipatory period), without compromising classification accuracy. We then improve upon these recognition rates by using a source localization technique based on the biophysical model of the human head. We further validate the feasibility of recognizing CNV potentials in an online experiment, and report for the first time that, under controlled conditions, these potentials can be reproduced and recognized in realistic interaction scenarios (assistive technology web-browsing) with high accuracies. Second, the thesis provides insights into the selection of appropriate preprocessing stages required for improving the SNR of SCPs. The CNV potentials are characterized by low frequencies that are usually recorded with full-DC, and hence suffer from task-irrelevant high amplitude fluctuations and spatial noise. To account for this, we identified appropriate spectral and spatial filters to improve the SNR. We demonstrate the potential of these preprocessing stages by using fusing multiple electrode specific linear classifiers, which achieve recognition performances of 90±2% (area under curve of receiver operating characteristic), where the classifiers are trained using recordings from one day and tested on the recordings from several days apart. Finally, the thesis identifies different facets of anticipatory behavior. Apar