Cognitive attention behaviour detection systems using Electroencephalograph (EEG) signals

Using EEG signals to estimate cognitive intellectual state has pinched increasing attention in recent years, especially in the framework of brain-computer interface (BCI) design. Nevertheless, this goal is extremely difficult because, in addition to the multifaceted relationships between the cognitive state and EEG signals that yields the non-stationarity of the features extracted from EEG signals, there are artefacts bring together by eye blinks and head and body motion. In this paper, we present a review of such Cognitive Attention behaviour estimation system, which can estimate the subject's cognitive state from the measured EEG signals. In most of the systems, a mutual information based method is employed to diminish the dimensionality of the features as well as to increase the robustness of the system. Classifiers were implemented and the results are taken to be the final decisions. The results of an introductory test with data from freely moving subjects performing numerous tasks as opposed to the strictly controlled experimental set-ups of BCI provide strong support for this approach.