Published Online: Aug 27, 2015
Page range: 219 - 225
Received: Jan 09, 2015
Accepted: Aug 12, 2015
DOI: https://doi.org/10.1515/msr-2015-0030
© by V. Salai Selvam
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
The scalp electroencephalography (EEG) signal is an important clinical tool for the diagnosis of several brain disorders. The objective of the presented work is to analyze the feasibility of the spectral features extracted from the scalp EEG signals in detecting brain tumors. A set of 16 candidate features from frequency domain is considered. The significance on the mean values of these features between 100 brain tumor patients and 102 normal subjects is statistically evaluated. Nine of the candidate features significantly discriminate the brain tumor case from the normal one. The results encourage the use of (quantitative) scalp EEG for the diagnosis of brain tumors