A Steady-State Visual Evoked Potential Brain-Computer Interface System Evaluation as an In-Vehicle Warning Device

This thesis is part of current research at Center for Intelligence Systems Research (CISR) at The George Washington University for developing new in-vehicle warning systems via Brain-Computer Interfaces (BCIs). The purpose of conducting this research is to contribute to the current gap between BCI and in-vehicle safety studies. It is based on the premise that accurate and timely monitoring of human (driver) brain's signal to external stimuli could significantly aide in detection of driver's intentions and development of effective warning systems. The thesis starts with introducing the concept of BCI and its development history while it provides a literature review on the nature of brain signals. The current advancement and increasing demand for commercial and non-medical BCI products are described. In addition, the recent research attempts in transportation safety to study drivers' behavior or responses through brain signals are reviewed. The safety studies, which are focused on employing a reliable and practical BCI system as an in-vehicle assistive device, are also introduced. A major focus of this thesis research has been on the evaluation and development of the signal processing algorithms which can effectively filter and process brain signals when the human subject is subjected to Visual LED (Light Emitting Diodes) stimuli at different frequencies. The stimulated brain generates a voltage potential, referred to as Steady-State Visual Evoked Potential (SSVEP). Therefore, a newly modified analysis algorithm for detecting the brain visual signals is proposed. These algorithms are designed to reach a satisfactory accuracy rate without preliminary trainings, hence focusing on eliminating the need for lengthy training of human subjects. Another important concern is the ability of the algorithms to find correlation of brain signals with external visual stimuli in real-time. The developed analysis models are based on algorithms which are capable of generating results for real-time processing of BCI devices. All of these methods are evaluated through two sets of recorded brain signals which were recorded by g.TEC CO. as an external source and recorded brain signals during our car driving simulator experiments. The final discussion is about how the presence of an SSVEP based warning system could affect drivers' performances which is defined by their reaction distance and Time to Collision (TTC). Three different scenarios with and without warning LEDs were planned to measure the subjects' normal driving behavior and their performance while they use a warning system during their driving task. Finally, warning scenarios are divided into short and long warning periods without and with informing the subjects, respectively. The long warning period scenario attempts to determine the level of drivers' distraction or vigilance during driving. The good outcome of warning scenarios can bridge between vehicle safety studies and online BCI system design research. The preliminary results show some promise of the developed methods for in-vehicle safety systems. However, for any decisive conclusion that considers using a BCI system as a helpful in-vehicle assistive device requires far deeper scrutinizing

Experience dependent plasticity of cortical attention states

Modulation of sensory processing by attention occurs in part through the regulation of cortical oscillations in sensory cortex. The attentive or aroused state is generated by high-frequency gamma oscillations while during inattentive state cortical activity is dominated by low-frequency oscillations. It is unknown how or if this cortical state modulation is affected by changes in sensory experience. In this research, we study movement modulation of cortical oscillations in the visual cortex of rodents as a model for human selective attention. We use binocular eye-suturing in c57bl/6 mice as a model of visual deprivation in human, such as an early cataract, and study its effects through critical period. In eye-suture (ES) animals both eyelids are sutured before eye opening (EO). To assess cortical state regulation we obtain extracellular recordings of local field potentials (LFPs) and multi-unit activities (MUAs) using multi-electrode arrays in mice trained to run on a treadmill. Our preliminary evidence suggests that in control animals motion robustly amplifies gamma rhythms and decreases slow wave activities as early as the critical period for ocular dominance plasticity, a key developmental time for organization of thalamic afferent. This modulation of cortical state by movement was negligible in ES animals suggesting that normal visual experience is necessary for the development of cortical states. As expected, firing rates in ES animals were lower than control animals, showing the ES reduced excitatory drive to cortex. Thus our results suggest that cortical state regulation important for attention is either disrupted or delayed following deprivation of patterned vision. Further experiments will distinguish between these two possibilities and define the role of plasticity in the establishment of normal cortical oscillation