UC San Diego

This work develops and tests two Brain-Computer Interface (BCI) systems. The first one is a Steady-state visual evoked potential (SSVEP)-based BCI system. This thesis explores every component of an SSVEP-based BCI system from the front-end to back-end, including the visual stimuli, electroencephalogram (EEG) data acquisition, signal processing and a modularized platform. This thesis also discusses how to move an SSVEP-based BCI system from a laboratory demonstration to a real-life application. Our neurological results show that : (1) it is feasible to precisely render visual stimuli on mobile devices; (2) the signal quality of the SSVEPs measured from non-hair- bearing areas was comparable with, if not better than, that measured from hair-covered occipital areas; (3) it is practical to build a truly portable and wearable SSVEP- based BCI system integrating dry EEG sensors, miniature electronics, wireless telemetry, online signal-processing pipeline, and visual stimuli presentation on a smartphone. This work may significantly improve the practicality of an SSVEP-based BCI system for either real-life or clinical research. The second one is an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System for detecting and mitigating driving fatigue. In this thesis we translate the above-mentioned BCI platform to develop and test an OCLDM System that mitigate transient fatigue during a sustained attention task in a simulated driving environment. This system features a mobile wireless dry- sensor EEG headgear and a smartphone-based real-time EEG processing platform. The on-line testing results of the OCLDM System demontrated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments