Assistive technologies for characterizing subjective experience

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Individuals who are unable to move and speak rely on assistive technologies to convey their subjective experience to others. Such technologies translate physiological signals from the autonomic nervous system and the brain into outputs related to cognitive activity and emotional state. While systems to convey subjective experience are in their nascent stages, translating current state-of-the-art into practical systems that can be used in real-world contexts requires overcoming significant technical challenges in pattern recognition, signal processing and physiological signal classification. Addressing these challenges will enable the detection of the level of an individual's subjective experience, and its cognitive and affective content. Moreover, developing these technologies will advance our understanding of the physiological correlates of consciousness, cognition and emotion.***To characterize subjective experience, this research proposes to develop novel techniques and algorithms to classify electroencephalographic (EEG) and autonomic signals. A team of 2 doctoral and 2 masters students, assisted by 10 undergraduate summer students will i) assess levels of subjective experience by applying graph theoretical principles to compare EEG collected from individuals with and without anesthesia; ii) determine the cognitive content of subjective experience by developing single-trial event-related potential classification algorithms that can be used for cognitive testing; and iii) determine the affective content of subjective experience by developing an accurate user-independent emotion classification system from a large dataset of physiological signals. The algorithms and software developed in pursuit of these goals will lead to knowledge advances in neurophysiology, technical advances in physiological signal processing and the development of commercial products that have immediate relevance to our health care system ***This research program is of considerable importance to Canada, given our society's need for novel assistive technologies to care for the rising number of unresponsive individuals due to severe disability, dementia and traumatic injury. Such technologies will enable us to understand the subjective experience of these individuals, and will likely reduce the rates of caregiver burnout and abandonment, improve treatment, and reduce the significant cost of caring for this vulnerable population. More immediately, this research provides an exciting program for trainees to gain interdisciplinary expertise in techniques with broad academic and industrial relevance, enabling contributions to Canadian society through biomedical engineering career paths. **