Review of Neural Networks in the EEG Signal Recognition

Abstract

In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

Review of Neural Networks in the EEG Signal Recognition

“…Rodrigues et al [9] used neural networks for recognizing diseases such as alcoholism, Bi et al [10] for recognizing early Alzheimer's disease, Shim et al [11] for detect schizophrenia disease, Bagheri et al [12] for recognizing the inter-period epileptiform disease. Čukić et al [13] used machine learning to detect depression. It has been experimentally proven that using the Higuchi fractal dimension, and selective entropy will reveal participants diagnosed with depression.…”

“…Chambayil et al [44] and Song et al [45] presented a description of a study on the topic of determining the processes occurring in an EEG using neural networks is. The work is interesting from a technical point of view.…”