Electroencephalography
Future research

In research, currently EEG is often used in combination with machine learning. EEG data are pre-processed then passed on to machine learning algorithms. These algorithms are then trained to recognize different diseases like schizophrenia, epilepsy or dementia. Furthermore, they are increasingly used to study seizure detection. By using machine learning, the data can be analyzed automatically. In the long run this research is intended to build algorithms that support physicians in their clinical practice and to provide further insights into diseases. In this vein, complexity measures of EEG data are often calculated, such as Lempel-Ziv complexity, fractal dimension, and spectral flatness. It has been shown that combining or multiplying such measures can reveal previously hidden information in EEG data.
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