EEG analysis

<p>EEG analysis is exploiting mathematical <a href="/facts/Signal_processing/Npaja6zb">signal analysis methods</a> and computer technology to extract information from <a href="/facts/Electroencephalography/XGvpcBl5">electroencephalography</a> (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the <a href="/facts/Human_brain/qfSwSB8K">brain</a>; assist <a href="/facts/Physician/qJ3woFvG">physicians</a> in <a href="/facts/Medical_diagnosis/J47rvb0p">diagnosis</a> and <a href="/facts/Therapy/2ytkGbgT">treatment</a> choices; and to boost <a href="/facts/Brain%E2%80%93computer_interface/wdXWqFVr">brain-computer interface</a> (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a <a href="/facts/Mathematical_model/CGhyuxgz">mathematical model</a> is exploited to fit the sampled EEG signals, the method can be categorized as <a href="/facts/Parametric_statistics/Uvq9SJV9">parametric</a>, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: <a href="/facts/Time_domain/1w6mP2MU">time domain</a>, <a href="/facts/Frequency_domain/pyTVrUSq">frequency domain</a>, <a href="/facts/Time-frequency_domain/oeysrIKN">time-frequency domain</a>, and <a href="/facts/Nonlinear_system/4aYItALC">nonlinear</a> methods. There are also later methods including <a href="/facts/Deep_learning/JLuwD3ea">deep neural networks</a> (DNNs).
</p><p>Although it is extremely important for researchers to choose the appropriate EEG analysis methods according to their research objectives and the results they want to obtain, the finalized studies provide reference for future research, help solve existing problems and prepare the ground for future studies.  
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EEG analysis open-in-new

EEG analysis