The use of Evolutionary Algorithm- based methods in EEG based BCI systems

Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brainwaves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of such system through providing a better sub-set of electrodes or features, reducing the required training time of the classifiers, reducing the noise to signal ratio, and so on. This chapter provides a survey on some of the commonly used EA methods in EEG study. INTRODUCTION Motor nerves and muscles used in nervous system are traditional communication channel for interaction between brain and computer. A Brain Computer Interface (BCI) is a communication device that bypasses the peripheral nervous system and derives intention directly from brain activity, which it then translates into executable commands. A formal definition for BCI proposed at the first International BCI Meeting (Rensselaerville, New York, 1999) is “A brain-computer interface is a communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles” (Wolpaw et al., 2000). BCI devices typically incorporate stages such as signal acquisition, feature extraction, and classification in their operation. Electroencephalogram (EEG) is one of the commonly used non-invasive techniques in BCI for signal acquisition. EEG records variations of the surface potential from the scalp using some electrodes. The recorded signal is expected to reflect the functional activity of the underlying brain. The EEG signal is a mixture of signals that includes the desired brain activity (the summated signal of millions of cells in the cortex), as well as the heartbeat, eye movement, voluntary and involuntary muscle activity and some possible noise. Feature extraction stage is used to provide alternative representations of the raw measured signals that help the classifier to better discriminate the set of BCI operations. It is common to use preprocessing stage containing activities such as re-referencing electrodes, demeaning, normalizing, dimension reduction, artifact removal, and so on prior to feature extraction.