Literature

Power Spectrum Analysis of EEG Signals for Estimating Visual Attention [Volume 42– No.15, March 2012]

       It is well known fact that the EEG signals are the measure of the vigilance state of brain which changes according to task performed by a person. These changes are classified into few different frequency bands named as delta, theta, alpha, beta and gamma. The accurate classification of electrical activity for a particular state of human brain helps in neurological diagnosis and also for establishing standards for instrumentation development. This classification also helps in the brain computer interfacing which has been gaining wide attraction in the research industry. On this line of research, several studies have been conducted like correlation of awake and sleep states of brain, estimation of visual and audio alertness. Task related to alertness of human controller of ship, airplane, truck, rail are analyzed with the help of EEG signals. For this, mathematical transform and artificial neural network techniques have been used.
     The power spectrum analysis has been also used in the research mentioned earlier but in complex form and also found dependent upon error rate variation in time. Power estimation and changes in EEG signals related to muscle fatigue over the motor cortex area for Adductor Pollicis muscle measured in Relaxed and contraction states are classified using change in power spectrum, studies like Test of Variables of Attention (TOVA) was performed with power estimation and PCA to measure alertness. Feature extraction also done with the help of power spectral entropy in case to recognize left and right hand movement.

Reference: Mitul Kumar Ahirwal and Narendra D londhe, “Power Spectrum Analysis of EEG Signals for Estimating Visual Attention”, 2012, Volume 42– No.15 

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