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The existing bio-signal monitoring systems are mostly designed for signal recording without the capability of automatic analysis so that their applications are limited. The goal of this paper is to develop a real-time wireless embedded electroencephalogram (EEG) monitoring system that includes multi-channel physiological acquisition, wireless transmission, and an embedded system. The wireless transmission can overcome the inconvenience of wire routing and the embedded multi-task scheduling for the dual-core processing system is developed to realize the real-time processing. The whole system has been applied to detect the driver's drowsiness for demonstration since drowsiness is considered as a serious cause of many traffic accidents. The electroencephalogram (EEG) features changes from wakefulness to drowsiness are extracted to detect the driver's drowsiness and an on-line warning feedback module is applied to avoid disasters caused by fatigue. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate Electroencephalography dynamics in response to kinesthetic stimuli during driving. We used a Virtual Reality driving simulator consisted of a hydraulic hexapod motion platform to create practical driving events. We compared the EEG dynamics in response to kinesthetic stimulus while the platform was in motion, to that while the platform was stationary. The scalp-recorded EEG channel signals were first separated into independent brain sources using Independent Component Analysis (ICA), and then studied with time-frequency analysis. Our results showed that independent brain processes near the somatomotor cortex exhibited alpha power decreases across sessions and subjects. Negative potentials phase-locked to the onsets of deviation events under motion conditions were observed in a central midline component. The results allow us to better understand different brain networks involved in driving, and provide a foundation for studying event-related EEG activities in the presence of kinesthetic stimuli. [ABSTRACT FROM AUTHOR]

Because satellite images usually contain many complex factors and mix-up samples, a high recognition rate is not easy to attain. Especially for a nonhomogeneous region, the gray values of its satellite image vary greatly, and thus the direct use of gray values cannot do the categorization task correctly. Classification of terrain cover using polarimetric radar is an area of considerable current interest and research. Without the benefit of satellite, we cannot analyze the information of the distribution of soils and cities for a land development, as well as the variation of clouds and volcano for weather forecasting and for precaution, respectively. This chapter discusses a hybrid neural fuzzy network, combining unsupervised and supervised learning, for designing classifier systems. Based on systematic feature analysis, which is crucial for data mining and knowledge extraction, the proposed scheme signifies a novel algebraic system identification method, which can be used for knowledge extraction in general, and for satellite image analysis in particular. The goal of this chapter is to develop a cascaded architecture of a neural fuzzy network with feature mapping (CNFM) to help the classification of satellite images. [ABSTRACT FROM AUTHOR]