Citations are important for a journal to get impact factor. Impact factor is a measure reflecting the average number of citations to recent articles published in the journal. The impact of the journal is influenced by impact factor, the journals with high impact factor are considered more important than those with lower ones. Impact factor plays a major role for the particular journal. Journal with higher impact factor is considered to be more important than other ones. Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used, as in electrocorticography. Notwithstanding recent advances in neuroimaging, EEG remains a major technique for investigation of the brain. Its main applications are in assessment of cerebral function rather than for detecting structural abnormalities. The principal clinical applications are in epilepsy, states of altered consciousness including postanoxic and traumatic coma, the parasomnias, dementias, toxic confusional states, cerebral infections, and various other encephalopathies. Abnormalities in EEG reflect general pathophysiological processes, raised intracranial pressure, cerebral anoxia, or oedema, epileptogenesis etc, and show little specificity for a particular disease. Consequently, they need to be interpreted in a particular clinical context; the use of routine EEG examination for screening purposes is rarely of value. Conversely, the investigation becomes most cost effective when applied to specific problems--for instance, monitoring serial changes in postanoxic coma or during open heart surgery, differential diagnosis (by telemetric ictal recordings) of epileptic and non-epileptic attacks, and providing early prediction of outcome after stroke. High technological standards and an individualised problem solving approach are prerequisites of a cost effective, reliable clinical EEG service. These are most likely to be achieved by a considered, selective referral policy, the use where necessary of prolonged complex procedures such as telemetry, and the avoidance of routine examinations of dubious clinical relevance. EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals,[1] the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods.[2] There are also later methods including deep neural networks (DNNs). Frequency domain analysis, also known as spectral analysis, is the most conventional yet one of the most powerful and standard methods for EEG analysis. It gives insight to information contained in the frequency domain of EEG waveforms by adopting statistical and Fourier Transform methods.[3] Among all the spectral methods, power spectral analysis is the most commonly used, since the power spectrum reflects the 'frequency content' of the signal or the distribution of signal power over frequency. There are two important methods for time domain EEG analysis: Linear Prediction and Component Analysis. Generally, Linear Prediction gives the estimated value equal to a linear combination of the past output value with the present and past input value.