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Many individuals with severe motor disabilities may not be able to use conventional means of emotion expression (e.g. vocalization, facial expression) to make their emotions known to others. Lack of a means for expressing emotions may adversely affect the quality of life of these individuals and their families. The main objective of this thesis was to implement a non-invasive means of identifying emotional arousal (neutral vs. intense) and valence (positive vs. negative) by directly using brain activity. In this light, near infrared spectroscopy (NIRS), which optically measures oxygenated and deoxygenated hemoglobin concentrations ([HbO2] and [Hb], respectively), was used to monitor prefrontal cortex hemodynamics in 10 individuals as they listened to music excerpts. Participants provided subjective ratings of arousal and valence. With respect to valence and arousal, prefrontal cortex [HbO2] and [Hb] were characterized and significant prefrontal cortex hemodynamic modulations were identified due to emotions. These modulations were not significantly related to the characteristics of the music excerpts used for inducing emotions. These early investigations provided evidence for the use of prefrontal cortex NIRS in identifying emotions. Next, using features extracted from [HbO2] and [Hb] in the prefrontal cortex, an average accuracy of 71% was achieved in identifying arousal and valence. Novel hemodynamic features extracted using dynamic modeling and template-matching were introduced for identifying arousal and valence. Ultimately, the ability of autonomic nervous system (ANS) signals including heart rate, electrodermal activity and skin temperature to improve the identification results, achieved when using PFC [HbO2] and [Hb] exclusively, was investigated. For the majority of the participants, prefrontal cortex NIRS-based identification achieved higher classification accuracies than combined ANS and NIRS features. The results indicated that NIRS recordings of the prefrontal cortex during presentation of music with emotional content can be automatically decoded in terms of both valence and arousal encouraging future investigation of NIRS-based emotion detection in individuals with severe disabilities.

Although markets are emerging for commercial off-the-shelf components (such as Sun JavaBeans), there are many barriers to widespread component adoption. This is due to the inherent `black-box' nature of software components: developers have no knowledge or control of the component's internal characteristics. Without source or design details, developers only have the component's interface, documentation and test results to answer important questions about reliability, proper use, behavior andperformance. The current best practice of specifying a component's capabilities by providing only the syntax and informal documentation is insufficient to assemble mission or safety-critical systems successfully. To address these problems we have developed a framework forcreating and analyzing the concise specifications of components and their related interfaces. The framework extends a formal model for software architecture descriptions to support the specification of a range of terms. With formal component specifications developers can use the framework to analyze the properties of individual components or of entire systems. Unlike other approaches, the formal basis and implementation of our framework enhance understanding and automates much of thecomponent analysis process.