Electroencephalogram (EEG) is an electrophysiological monitoring approach to record electrical activity from the human brain, which normally been used to study the brain activities. As EEG is a multichannel signal with high temporal resolution, EEG gives us a huge amount of data. Thus, to fully utilize the information from EEG, researchers have provided better alternatives such as automated processing that can analyze EEG more efficiently. In the development of these approaches, informative features, which can be extracted mathematically, have to be extracted from the EEG. Therefore, this paper reports features that have been used by researchers in order to represent the information in the EEG based on a literature study that have been done. Amongst these features are the spectral analysis features, entropy, and statistical features.
Volume 11 | 03-Special Issue
Pages: 1781-1787