Analysis of EEG Using Multilayer Neural Network with Multi-Valued Neurons

Abstract

There is a wealth of analysis techniques that can be used in analyzing data of such a nature as EEG (Electroencephalogram), yet there are still many more ways and possibilities of analysis techniques to consider in order to produce a method that far exceeds the capabilities of the prevalent method. Since a multilayer neural network with multi-valued neurons (MLMVN) was successfully used earlier to decode EEG signals in a brain/computer interface (BCI) by analysis of their Fourier transform, it seemed very attractive to use it as a tool for EEG analysis. This work aims to further investigate how a complex-valued machine learning tool can be used to analyze EEG in the frequency domain. Our goal was to check how Fourier transform and complex wavelet transform of EEG can be analyzed using MLMVN in order to diagnose epilepsy, its remission or absence. We worked with a commonly used benchmark data set of epilepsy-related EEGs. The analysis of the transformed data was performed to determine a set of relevant statistical characteristics of DTCWT and Fourier transform components, which were then used as inputs of the MLMVN. The obtained results show a very high efficiency of the proposed approach.

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

“…Complex feature vectors represent periodic/cyclic data, such as oscillation and wave, e.g., electromagnetic phenomena, electric circuits, acoustic/biomedical signals, and imaging radar, in a natural way. Complex valued neural networks (CVNN) have been used for array signal analysis [ 32 , 33 ], radar image processing [ 34 , 35 ], and EEG analysis [ 36 , 37 , 38 ].…”