Electroencephalogram （EEG） based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain＇s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based English speller （EEGES） has been widely studied in recent years, few studies have focused on the EEG-based Chinese speller （EEGCS）. The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.
Consciousness research has been of great concern to philosophers, psychologists and neuroscientists in recent years. At the same time, consciousness has also attracted more and more interest of artificial intelligence （AI） researchers. In order to make more intelligent machines, many computing models of machine consciousness have been presented. Furthermore, self-consciousness has relevance to the level of intelligent functions. Hence, it is necessary to study self-consciousness in AI. This thesis, starting from biological consciousness, discusses some viewpoints of machine consciousness. Based on the discussions, we present a way to emulate self-consciousness and test this method via simulation experiments. Our results indicate that self-consciousness, which belongs to organisms, can he imitated by machines.
