For several years, many efforts have been done to use the electro-encephalogram (EEG) as a new communication channel between human brain and computer. This new communication channel is called EEG-based brain-computer interface (BCI). The aim of brain-computer interface More
For several years, many efforts have been done to use the electro-encephalogram (EEG) as a new communication channel between human brain and computer. This new communication channel is called EEG-based brain-computer interface (BCI). The aim of brain-computer interface (BCI) research is to establish a new communication channel that directly translates brain activities into sequences of control commands for an output device such as a computer application or a neuroprosthesis. The major advantage of EEG-based BCI is that no physical movement is required. The motor imagery is the essential part of the most EEG-based communication systems. One of the major problems in developing a real-time Brain Computer Interface (BCI) is the eye blink artifact suppression. Recently, a more effective method has been introduced for removing a wide variety of artifacts from multi-channel EEG signals based on blind source separation by Independent Component Analysis (ICA). However, the method requires visual inspection of ICA components and manual classification of the interference components. This can be time-consuming and is not desirable for real-time artifact suppression. Moreover, the real-time application of this method for artifact rejection has not been considered so far. In this paper, various ICA methods with adaptive learning algorithm are presented and evaluated by computer simulation. The results from real-data demonstrate that the proposed scheme removes perfectly eye blink artifacts from the contaminated EEG signals and is suitable for use during on-line EEG monitoring and EEG-based brain computer interface.
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