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The primary goal of this paper is to discover the definition of quantitative EEG and understand its areas of application. Moreover, its role and importance are underlined in the context of the research field. Nonetheless, the risks are also reported. In the end, the conclusions are drawn. Firstly, quantitative EEG is aimed to evaluate the operational abilities of the brain and uses numerical values and ratios to measure the condition of a patient (Gudmundsson et al. 2162).
Moreover, its function can be specified to the ability to introduce consistent interpretation between the connection of brain activity and the external world (Hong, Goa and Goa 193). Primarily, it provides the topographic image of the brain electrophysiological data (Tong and Thakor 2). In the end, its functioning is rather complicated, as the brain activities are hard to measure.
It could be said that the quantitative EGG is actively used in psychology and neuroscience, as it is aimed to measure brain activity. Nonetheless, it is also actively applied in neurology, psychophysiology, and brain development (Harmony e100). For instance, it is a potential medical tool to identify the presence of cerebral ischemia (Friedman and Claassen 1707). Moreover, this approach is utilized as a predictive biomarker for Parkinson disease dementia (Klassen et. al 118). The last example is the fact that quantitative EEG contributes to the sufficient planning of the surgeries, for instance, in the context of epilepsy (Krsek et al. e42). It could be said that its area of application is vast, and its role in medical research and treatment is significant.
Nonetheless, is evident that the role and significance of quantitative EEG cannot be underestimated, as it is actively used to identify suitable treatment for the various illnesses and diseases. Moreover, it contributes to the advancement of clinical research and discoveries in the medical sphere. Nevertheless, it remains evident that this procedure involves various risks. The complexity of the quantitative EEG sometimes questions the validity of the data (Pei et al. 263).
However, the lack of validity can be decreased, as the quantitative EEG should be applied at the same time with the other methods to investigate the condition of the patient efficiently. Additionally, it is widely known that technologies tend to develop. In this case, this feature is beneficial for the future of quantitative EEG, as the risks related to the relevancy and validity of the information, which was collected during the procedure, can be reduced.
In conclusion, it could be said that quantitative EEG is a helpful tool for the treatment of various illnesses and diseases. It helps propose appropriate treatment, as the problems are identified. Its role cannot be underestimated, as it is actively applied in various medical spheres in the context of clinical research. Nonetheless, some risks tend to exist. However, it is widely known that continuous technological progress allows the development of particular techniques and technologies, which reduce the potential risks. It could be said that the future of EEG remains bright since it is one of the innovative ways to understand the nature and origin of the issues and illnesses. It will be actively used in the future as an analytical instrument to identify the condition of the patients.
Works Cited
Friedman, Daniel, and Jan Claassen. Quantitative EEG and Cerebral Ischemia. Clinical Neurophysiology 121.10 (2010): 1707-1708. Print.
Gudmudsson, Steinn, Thomas Runarsson, Sven Sigurdsson, Gudrun Eiriksdottir and Kristinn Johnsen. Reliability of Quantitative EEG Features. Clinical Neurophysiology 118.10 (2007): 2162-2171. Print.
Harmony, Terry. Quantitative EEG: Its Applications to Neurology, Psychophysiology, and Brain Development. Clinical Neurophysiology 119.9 (2008): e100. Print.
Hong, Yigin, Xiaorong Goa and Shangkai Goa 2015, Quantitative EEG-Based Brain-Computer Interface. Web.
Klassen, Bernard, James Hentz, Hannah Shill, Eric Driver-Dunckley, Vaugh Evidente, Michael Sabbagh, Chris Adler, Justin Caviness. Quantitative EEG as a Predictive Biomarker for Parkinson Disease Dementia. Neurology 77.2 (2011): 118-124. Print.
Krsek, Pavel, Radek Janca, Petr Jezdik, Tomas Havel, Roman Cmejla, Vladimir Komarek, Michal Tichy, Petr Marusic, Premysl Jiuska. Practical Value of EEG in Epileptical Surgery Planning. Clinical Neurophysiology 126.3 (2015): e42. Print.
Pei, Xiao-mei, Chong-xun Zeng, Wei-xing, Jin Xu. Quantitative Measure of Complexity of the Dynamic Event-Related EEG Data. Neurocomputing 70.1 (2006): 163-272. Print.
Tong, Shanbao, and Thakor, Nitish. Qualitative EEG Analysis Methods and Clinical Applications. London: Artech House, 2009. Print.
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