Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface  

作　　者：Julia Shen Baiyan Li Xuefei Shi 

机构地区：[1]Detroit Country Day, Beverly Hills, USA [2]College of Computer Science and Technology, Donghua University, Shanghai, China [3]Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, USA [4]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

出　　处：《Open Journal of Applied Sciences》2017年第3期98-113,共16页应用科学（英文）

摘　　要：In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

关 键 词：Brain-Computer Interface BRAIN Wave DROWSINESS Real-Time FOURIER TRANSFORM POLLING Algorithm 

分 类 号：R73[医药卫生—肿瘤]