Signal Processing in Human-Computer Interface

Human-Computer Interface;Bio signal processing;Drowsiness detection;EEG analysis;Alcohol and drug influence;Computer and Information Science;Computer and Information Science, College of Engineering and Computer Science

Song, TianyuHristova, Yulia ;

University of Michigan

关键词: Human-Computer Interface;    Bio signal processing;    Drowsiness detection;    EEG analysis;    Alcohol and drug influence;    Computer and Information Science;    Computer and Information Science, College of Engineering and Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/136619/TianyuThesis.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Low efficiency of the common Human-Computer Interface (HCI) such as keyboard and mouse are exposed when operations become more and more complex. It’s needed to study on advanced HCI like Brain-Computer Interface (BCI) to improve efficiency and develop new functions. BCI has potentials to be applied in driving systems and is usually used to detect the drowsiness of drivers. Once the driver is detected as being drowsy, a driving assistance system will alert him/her or the car becomes semi-autonomous.In the field of drowsiness detection, bio signal processing with a 10-20 System is the common method. Unfortunately, this method is not very accurate because of the limitation of the unknown knowledge about brain waves and judgement error on the true value of drowsiness state as well as the influence of many factors such as age, alcohol and drug. In this thesis, we studied a probabilistic neural network (PNN) based on Lempel-Ziv Complexity, Approximate Entropy, and a rational band feature. Experimental results indicate that 82% accuracy was achieved by using this PNN model for predicting the human drowsiness. We also investigated the eye movement analysis and alcohol influence on the drowsiness.