Contents I. Introduction
A Brain-Computer Interface (BCI) is a system in which user's intentions are conducted towards an external device or neural prosthesis or it may even be used to control Functional Electrical Stimulation (FES), not requiring any external execution. BCI systems, based on their mode of operation, are divided into two major classes: synchronous and asynchronous. Synchronous BCIs operate in a system controlled manner, where system orders the user when to start imagining executing a task. Signal processing in synchronous systems is limited within previously defined time windows, in which user is allowed to operate. In contrast, there are asynchronous systems which allow the user to produce motor related patterns whenever he/she wishes to. Here the neurophysiological signal e.g. EEG should be continuously monitored to be able to detect “event” related patterns from “idle” thinking. In these systems, the challenging issue is to distinguish the occurrence of motor related changes from spontaneous EEG, accurately enough to have a reliable brain-controlled switch. Furthermore, the design of an asynchronous BCI has been carried out so far in two major ways. One way has been the incorporation of event detection task into classification of motor activity; by thresholding the classifier's scores [1]. Hereby functions of synchronous and asynchronous systems are gathered in one system. The veritable point is that in these systems the errors related to detection of every class of motor activities are incorporated in system's total performance. Take the example of detection of right hand versus left hand movement; here the imperfection due to detection of right hand movement occurrence versus idling accumulates with imperfection due to detection of left hand activity versus idling. Therefore system's total performance would suffer from both simultaneously.
