Assistive technologies for characterizing subjective experience

无法移动和说话的人依靠辅助技术将他们的主观经验传达给其他人。这类技术将来自自主神经系统和大脑的生理信号转化为与认知活动和情绪状态相关的输出。虽然传达主观体验的系统还处于初级阶段，但将当前最先进的系统转化为可以在现实世界中使用的实用系统，需要克服模式识别、信号处理和生理信号分类方面的重大技术挑战。解决这些挑战将使人们能够检测到个人主观体验的水平，以及它的认知和情感内容。此外，发展这些技术将促进我们对意识、认知和情绪之间的生理关联的理解。*为了表征主观体验，本研究提出开发新的技术和算法来分类脑电(EEG)和自主神经信号。一个由2名博士生和2名硕士学生组成的团队，在10名本科生暑期学生的帮助下，将i)通过应用图论原理来评估主观体验的水平，以比较从有麻醉和无麻醉的个体收集的脑电；ii)通过开发可用于认知测试的单次试验事件相关电位分类算法来确定主观体验的认知内容；以及iii)通过从大量生理信号数据集开发准确的独立于用户的情绪分类系统来确定主观体验的情感内容。为追求这些目标而开发的算法和软件将导致神经生理学方面的知识进步、生理信号处理方面的技术进步以及与我们的医疗保健系统直接相关的商业产品的开发*鉴于我们的社会需要新颖的辅助技术来照顾由于严重残疾、痴呆症和创伤而导致的越来越多的无反应个人，该研究计划对加拿大具有相当重要的意义。这些技术将使我们能够了解这些人的主观体验，并可能降低照顾者疲惫和被遗弃的比率，改善治疗，并降低照顾这一弱势群体的显著成本。更直接的是，这项研究为学员提供了一个令人兴奋的计划，让他们在具有广泛学术和行业相关性的技术方面获得跨学科的专业知识，并通过生物医学工程的职业道路为加拿大社会做出贡献。**

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. **