| Summary: | 碩士 === 慈濟大學 === 醫學資訊學系碩士班 === 100 === The main purpose of this study is to use the single-channel embedded smart sensor to develop a brain cognition-aware pervasive computing systems. In general, people receive multiple messages from external environment by using sensory, vision, hearing, smelling, and tasting for perception. Our brains deal with problems through learning, memory and thinking, and these processes can be called context-awareness processing. When brains are thinking and solving problems, physiological reactions by using biomedical signals response those context-awareness processing.
This research investigated those in-class college students. Their physiological signal reactions from electroencephalogram (EEG),electro-oculogram (EOG) and the three-axis accelerometer during the classare recorded and analysis. In-class signal features were extracted and were classified by artificial neural networks, including principal component neural networks (PCANN), back propagation neural network (BPNN) and K-Dimensional Tree (Kd-tree) for understanding students' cognitive context in the class.
|
|---|
